Search Results (48)

Tyre wear particles (TWPs), generated from tyre-road abrasion, are a pervasive and under-regulated environmental pollutant, accounting for a significant share of global microplastic contamination. Recent estimates indicate that 1.3 million metric tons of TWPs are released annually in Europe, dispersing via atmospheric transport, stormwater runoff, and sedimentation to contaminate air, water, and soil. TWPs are composed of synthetic rubber polymers, reinforcing fillers, and chemical additives, including heavy metals such as zinc (Zn) and copper (Cu) and organic compounds like polycyclic aromatic hydrocarbons (PAHs) and N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD). These constituents confer persistence and bioaccumulative potential. While TWP toxicity in aquatic systems is well-documented, its ecological impacts on terrestrial environments, particularly in agricultural soils, remain less understood despite global soil loading rates exceeding 6.1 million metric tons annually. This review synthesizes global research on TWP sources, environmental fate, and ecotoxicological effects, with a focus on soil–plant systems. TWPs have been shown to alter key soil properties, including a 25% reduction in porosity and a 20–35% decrease in organic matter decomposition, disrupt microbial communities (with a 40–60% reduction in nitrogen-fixing bacteria), and induce phytotoxicity through both physical blockage of roots and Zn-induced oxidative stress. Human exposure occurs through inhalation (estimated at 3200 particles per day in urban areas), ingestion, and dermal contact, with epidemiological evidence linking TWPs to increased risks of respiratory, cardiovascular, and developmental disorders. Emerging remediation strategies are critically evaluated across three tiers: (1) source reduction using advanced tyre materials (up to 40% wear reduction in laboratory tests); (2) environmental interception through bioengineered filtration systems (60–80% capture efficiency in pilot trials); and (3) contaminant degradation via novel bioremediation techniques (up to 85% removal in recent studies). Key research gaps remain, including the need for long-term field studies, standardized mitigation protocols, and integrated risk assessments. This review emphasizes the importance of interdisciplinary collaboration in addressing TWP pollution and offers guidance on sustainable solutions to protect ecosystems and public health through science-driven policy recommendations. Full article

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into machine learning (ML) models for credit delinquency prediction. The approach is grounded in a CRISP-DM framework, combining stationarity testing (Dickey–Fuller), causality analysis (Granger), and post hoc explainability (SHAP, LIME), along with performance evaluation via AUC, ACC, KS, and F1 metrics. The empirical analysis uses nearly 8.2 million records compiled from multiple sources, including 367,000 credit operations granted to individuals and microbusiness owners by a regulated Peruvian financial institution (FMOD) between January 2020 and September 2023. These data also include time series of delinquency by economic activity, external factor indicators (e.g., mortality, climate disruptions, and protest events), and their dynamic interactions assessed through Granger causality to evaluate both the intensity and propagation of external shocks. The results confirm that EF inclusion significantly enhances model performance and robustness. Time-lagged mortality (COVID MOV) emerges as the most powerful single predictor of delinquency, while compound crises (climate and unrest) further intensify default risk—particularly in portfolios without public support. Among the evaluated models, CNN and XGB consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. Post hoc analyses reveal that EF effects dynamically interact with borrower income, indebtedness, and behavioral traits. This study provides a scalable, explainable framework for integrating systemic shocks into credit risk modeling. The findings contribute to more informed, adaptive, and transparent lending decisions in volatile economic contexts, relevant to financial institutions, regulators, and risk practitioners in emerging markets. Full article

In the whole service life of building clusters, they will encounter multiple hazards, including the disaster chain of earthquakes and building debris. The falling debris may block the post-earthquake roads and even severely affect the evacuation, emergency, and recovery operations. It is of great significance to develop a surrogate model for predicting seismic responses of building clusters as well as a prediction model of post-earthquake debris. This paper presents a general methodology for developing a surrogate model for rapid seismic responses calculation of building clusters and probabilistic prediction model of debris width. Firstly, the building cluster is divided into several types of representative buildings according to the building function. Secondly, the finite element (FE) method and discrete element (DE) method are, respectively, used to generate the data pool of structural floor responses and debris width. Finally, with the structural response data of maximum floor displacement, a surrogate model for rapidly calculating seismic responses of structures is developed based on the XGBoost algorithm, achieving R² > 0.99 for floor displacements and R² = 0.989 for maximum inter-story drift ratio (MIDR) predictions. In addition, an unbiased probabilistic prediction model for debris width of blockage is established with Bayesian updating rule, reducing the standard deviation of model error by 60% (from σ = 10.2 to σ = 4.1). The presented models are applied to evaluate the seismic damage of the campus building complex in China University of Mining and Technology, and then to estimate the range of post-earthquake falling debris. The results indicate that the surrogate model reduces computational time by over 90% compared to traditional nonlinear time-history analysis. The application in this paper is helpful for the development of disaster prevention and mitigation policies as well as the post-earthquake rescue and evacuation strategies for urban building complexes. Full article

Sanitary sewer pipelines frequently experience blockages, structural failures, and overflows, underscoring the dire state of U.S. wastewater infrastructure, which has been rated a D-, while America’s overall infrastructure scores only slightly better at C-. Traditional open-trench excavation methods or excavation technology (ET) for replacing deteriorated pipes are notoriously expensive and disruptive, requiring extensive processes like route planning, surveying, engineering, trench excavation, pipe installation, backfilling, and ground restoration. In contrast, trenchless technologies (TT) provide a less invasive and more cost-effective alternative. Among these, cured-in-place pipe technology (CIPPT), which involves inserting resin-impregnated fabric into damaged pipelines, is widely recognized for its efficiency. However, a comprehensive life cycle cost analysis (LCCA) directly comparing ET and TT, accounting for the net present value (NPV) across installation, maintenance, and rehabilitation costs, remains unexplored. This study aims to establish an LCCA framework for both CIPPT and ET, specifically for sanitary sewer pipes ranging from 8 to 42 inches in diameter. The framework incorporates construction, environmental, and social costs, providing a holistic evaluation. The key costs for ET involve pipe materials and subsurface investigations, whereas TT’s costs center around engineering and design. Social impacts, such as road and pavement damage, disruption to adjacent utilities, and noise, are pivotal, alongside environmental factors like material use, transportation, project duration, and equipment emissions. This comprehensive framework empowers decision makers to holistically assess economic and environmental impacts, enabling informed choices for sustainable sewer infrastructure renewal. Full article

5,6-dihydroxyflavone (5,6-DHF), a flavonoid that possesses potential anti-inflammatory and antioxidant activities owing to its special catechol motif on the A ring. However, its function and mechanism of action against inflammation and cellular oxidative stress have not been elucidated. In the current study, 5,6-DHF was observed inhibiting lipopolysaccharide (LPS)-induced nitric oxide (NO) and cytoplasmic reactive oxygen species (ROS) production with the IC₅₀ of 11.55 ± 0.64 μM and 0.8310 ± 0.633 μM in murine macrophages, respectively. Meanwhile, 5,6-DHF suppressed the overexpression of pro-inflammatory mediators such as proteins and cytokines and eradicated the accumulation of mitochondrial ROS (mtROS). The blockage of the activation of cell surface toll-like receptor 4 (TLR4), impediment of the phosphorylation of c-Jun N-terminal kinase (JNK) and p38 from the mitogen-activated protein kinases (MAPK) pathway, Janus kinase 2 (JAK2) and signal transducer and activator of transcription 3 (STAT3) from the JAK-STAT pathway, and p65 from nuclear factor-κB (NF-κB) pathways were involved in the process of 5,6-DHF suppressing inflammation. Furthermore, 5,6-DHF acted as a cellular ROS scavenger and heme-oxygenase 1 (HO-1) inducer in relieving cellular oxidative stress. Importantly, 5,6-DHF exerted more potent anti-inflammatory activity than its close structural relatives, such as baicalein and chrysin. Overall, our findings pave the road for further research on 5,6-DHF in animal models. Full article

New technologies are being used to facilitate the recognition process during and after earthquakes. These advanced tools are essential to keep track of what is left from of the destruction suffered by the built stock. Among the new technologies are video recordings captured during seismic events, footage from drones, and satellite imagery acquired before and after the event. This review paper presents a series of examples collected from the 2023 Türkiye–Syria earthquakes to illustrate how these new technologies offer a unique and efficient way to capture, document, and transfer information among experts in seismology, earthquake engineering, and disaster management. Whenever possible, these examples are accompanied by simple qualitative explanations to enhance understanding. To demonstrate the potential of video cameras and drone imagery for quantitative analysis, in addition to the various simple examples provided, two case studies are provided—one on road blockages, and another on intensity assessment and wave attenuation as observed in video cameras. These technologies are critical and merit considerable focus, particularly video cameras, which have not received much attention recently, on helping to understand seismic wave passage and their impact on the built environment. Enhancing our use of video cameras in this context can significantly contribute to the sustainability and resilience of our society. With the rapid advancement of image analysis, we advocate for a collaborative platform for accessing and utilizing imagery materials, aiding current and future generations in analysing the causes of such tragedies. Full article

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and property of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient, accurate, and rapid assessment of high-risk landslide zones carries important implications for decision making in disaster response and for mitigating potential secondary disasters. The high-intensity zones VII to IX of the Luding Ms6.8 earthquake on 5 September, 2022, were used as a case study here. Based on the simple Newmark model, the difference method and the cumulative displacement method were employed to assess earthquake-triggered landslides. The assessment results from both methods demonstrated that the areas posing an extremely high risk of earthquake-triggered landslides were predominantly situated on the western side of the Xianshuihe Fault. Verification using actual landslide data showed that both methods had high predictive accuracy, with the difference method slightly outperforming the cumulative displacement method. Moreover, this study recommends determining threshold values for each landslide risk interval having physical meanings using previous data on strong earthquakes when utilizing the difference method to assess the risk of earthquake-triggered landslides. Full article

The drainage capacity of stormwater inlets, which serve as the connection between the surface and the underground drainage system, directly affects surface runoff and the drainage capacity of underground drainage systems. However, in reality, stormwater inlets are often blocked due to the accumulation of leaves, human waste disposal and other factors, resulting in a greatly reduced drainage capacity of the drainage network and, in turn, urban waterlogging disasters. In view of the problem of stormwater inlet blockage, employing a typical waterlogging point in the Lianjiang Middle Road area of Fuzhou city as the research object, the stormwater inlet equivalent drainage method was adopted in this paper to characterize the drainage capacity of the pipe network and enable the control of the stormwater inlet blockage state. Coupled with the stormwater inlet drainage equation, an improved ITF-FLOOD two-dimensional hydrodynamic model was constructed, and the influence of stormwater inlet blockage on urban waterlogging under different rainfall return periods was simulated and analyzed. With increasing rainfall return period, the influences of stormwater inlet blockage on both the maximum area and the depth of accumulated water in the study area gradually decreased compared with those of a nonblocked stormwater inlet, and the growth proportions decreased from 43.35% and 34.58% under the 1-year rainfall scenario to 3.34% and 9.76% under the 50-year rainfall scenario, respectively. However, in terms of the change in the accumulated water level, stormwater inlet blockage will cause an increase, and the influence will always be significant. Overall, stormwater inlet blockage aggravated the waterlogging risk and the extent of waterlogging. Therefore, the results provided a reference for the construction of an urban waterlogging model and have certain guiding significance for waterlogging prevention and control in the study area prone to stormwater inlet blockage. Full article

The difference between waterway traffic and road traffic in terms of lane lines leads to the direct application of the method of plotting the fundamental diagram of road traffic flow to waterway traffic, and it is difficult to reveal the mechanism of waterway traffic flow operations. This study proposes a shipping-lane-subdivision approach to tackle this problem. Additionally, it introduces a more suitable fundamental diagram-plotting method for waterway traffic based on the aforementioned method. The southern channel in the estuary of the Yangtze River was taken as the research water, and the fundamental diagram of traffic flow in this water was plotted to verify the similarities between the fundamental diagram of waterway traffic flow and the fundamental diagram of road traffic flow. Upon evaluating the plotted fundamental diagram, it was determined that the blockage density of the subdivided shipping lane is around 6.5 vessels per nautical mile. This method has significant potential for its application in the theory of waterway traffic flow. Full article

Congestion persists despite various demand management techniques, particularly for handling recurrent congestion. However, non-recurrent congestion from events like VIP movements poses unique challenges, especially during peak hours. This study investigates the environmental and economic impacts of road blockages due to VIP movements in developing countries, focusing on Pakistan. Considering practiced standard operating procedures associated with VIP movements, this study finds significant delays and economic burdens in debt-ridden economies. It uses discrete choice modeling and microsimulation techniques to evaluate the value of travel time and quantifies road blockage effects on fuel consumption, travel time, and carbon emissions. Data from central blockage locations in Rawalpindi and Islamabad reveal a value of travel time estimated at 1.77 USD/h, with income and gender significantly influencing mode choices during VIP movements. Moreover, road blockages exceeding two minutes substantially negatively impact the environment and economy, particularly in developing nations. Urgent action is needed for effective mitigation strategies and sustainable transportation policies to address the detrimental effects and promote alternative transportation modes. Recommendations include limiting VIP blockages to a maximum of two minutes and implementing policies to discourage private car usage. Despite limitations, the study emphasizes the critical role of sustainable transportation policies in enhancing the well-being of road users in developing nations. Full article

Geological landslide disasters significantly threaten the safety of people’s lives and property. Landslides are a significant threat in Zayu County, Tibet, resulting in numerous geological disasters, including the 1950 earthquake that caused significant casualties and river blockages. More recent landslides have caused substantial economic losses and infrastructure damage, posing ongoing risks to the local population and their property. Landslide hazard assessment is a critical task in geological disaster prevention and mitigation. This study applied the Inception model to assess landslide hazard in the Zayu area. The Inception model excels at capturing multi-scale features efficiently through its architecture. Fifteen disaster-causing factors were selected as the primary indicators for landslide susceptibility assessment. On this basis, the Inception model was used for landslide susceptibility assessment. Combined with daily precipitation data in the Zayu area, the landslide hazard assessment of the “25 April 2010, heavy rainstorm in Zayu, Tibet” was completed. Back Propagation Neural Network (BPNN), Residual Neural Network (ResNet), Convolutional Neural Network (CNN), and Visual Geometry Group-16 (VGG-16) were introduced for comparison of the fitting effects, and SHapley Additive exPlanations (SHAP) was used for interpretability analysis. The comparative experimental results show that the Inception model performed best in landslide susceptibility assessment and is feasible in practical use. The results also show that the most critical factors in the model were topographic wetness index (TWI), normalized difference water index (NDWI), and road density. This study is significant for assessing landslide hazard in geological landslide disaster prevention and mitigation. It provides a reference for further research and response to similar disasters. Full article

The signal blockage and multipath effects of the Global Navigation Satellite System (GNSS) caused by urban canyon scenarios have brought great technical challenges to the positioning and navigation of autonomous vehicles. In this paper, an improved factor graph optimization algorithm enhanced by a resilient noise model is proposed. The measurement noise is resilient and adjusted based on an approximate Gaussian distribution-based estimation. In estimating and adjusting the noise parameters of the measurement model, the error covariance matrix of the multi-sensor fusion positioning system is dynamically optimized to improve the system accuracy. Firstly, according to the approximate Gaussian statistical property of the GNSS/odometer velocity residual sequence, the measured data are divided into an approximate Gaussian fitting region and an approximate Gaussian convergence region. Secondly, the interval is divided according to the measured data, and the corresponding variational Bayesian network and Gaussian mixture model are used to estimate the innovation online. Further, the noise covariance matrix of the adaptive factor graph-based model is dynamically optimized using the estimated noise parameters. Finally, based on low-cost inertial navigation equipment, GNSS, odometer, and vision, the algorithm is implemented and verified using a simulation platform and real-vehicle road test. The experimental results show that in a complex urban road environment, compared with the traditional factor graph fusion localization algorithm, the maximum improvement in accuracy of the proposed algorithm can reach 65.63%, 39.52%, and 42.95% for heading, position, and velocity, respectively. Full article

This paper presents a safety tool to assess the risk of road blockage during and after emergency situations, mainly due to earthquakes. This method can be used by public authorities to calculate the risk of road paths prone to blockage in case of seismic events. Typological classes of elements interfering with roads, such as unreinforced masonry and reinforced concrete buildings, unreinforced masonry and reinforced concrete bridges, retaining walls, and slopes, are considered. The mean annual frequency (MAF) of exceedance of a blockage limit state is calculated for a path with redundant road segments considering fragility curves from the literature. A practical example is presented for Amatrice, a town in Central Italy hit by the 2016 earthquake. After verifying that the MAF of exceedance demand is lower than the capacity for two roads, a strengthening solution is assumed for two buildings in the path, resulting in a reduction by more than 50% of the MAF demand. For a higher safety level, a bypass is proposed obtaining a demand/capacity ratios four orders of magnitude lower than that obtained with strengthening solutions, highlighting and quantifying the beneficial effect of removing vulnerable structures along the path. Full article

Autonomous vehicles (AVs) require accurate navigation, but the reliability of Global Navigation Satellite Systems (GNSS) can be degraded by signal blockage and multipath interference in urban areas. Therefore, a navigation system that integrates a calibrated Reduced Inertial Sensors System (RISS) with GNSS is proposed. The system employs a machine-learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) as a novel calibration technique to improve the accuracy and reliability of the RISS. The ANFIS-based RISS/GNSS integration provides a more precise navigation solution in such environments. The effectiveness of the proposed integration scheme was validated by conducting tests using real road trajectory and simulated GNSS outages ranging from 50 to 150 s. The results demonstrate a significant improvement in 2D position Root Mean Square Error (RMSE) of 43.8% and 28% compared to the traditional RISS/GNSS and the frequency modulated continuous wave (FMCW) Radar (Rad)/RISS/GNSS integrated navigation systems, respectively. Moreover, an improvement of 47.5% and 23.4% in 2D position maximum errors is achieved compared to the RISS/GNSS and the Rad/RISS/GNSS integrated navigation systems, respectively. These results reveal significant improvements in positioning accuracy, which is essential for safe and efficient navigation. The long-term stability of the proposed system makes it suitable for various navigation applications, particularly those requiring continuous and precise positioning information. The ANFIS-based approach used in the proposed system is extendable to other low-end IMUs, making it an attractive option for a wide range of applications. Full article

Traditional spalling repair on concrete pavement roads is labor-intensive. It involves traffic blockages and the manual calculation of repair areas, leading to time-consuming processes with potential discrepancies. This study used a line scan camera to photograph road surface conditions to analyze spalling without causing traffic blockage in an indoor setting. By using deep learning algorithms, specifically a region-based convolutional neural network (R-CNN) in the form of the Mask R-CNN algorithm, the system detects spalling and calculates its area. The program processes data based on the Federal Highway Administration (FHWA) spalling repair standards. Accuracy was assessed using root mean square error (RMSE) and Pearson correlation coefficient (PCC) via comparisons with actual field calculations. The RMSE values were 0.0137 and 0.0167 for the minimum and maximum repair areas, respectively, showing high accuracy. The PCC values were 0.987 and 0.992, indicating a strong correlation between the actual and calculated repair areas, confirming the high calculation accuracy of the method. Full article