Search Results (3,211)

To investigate the influence of segmental lining width on ground and tunnel deformation during shield tunneling beneath residential buildings, a numerical analysis model was established using Midas GTS NX based on the engineering context of the Guangzhou Metro Guanggang Xincheng depot tunnel underpassing residential structures. The simulation results were validated through comparison with field monitoring data, and a gray relational analysis was employed to quantitatively assess the sensitivity of various deformation indicators to segment width. The findings indicate that, under the engineering scenario of a shield tunnel crossing beneath residential buildings, the use of 1.2 m-wide segments is more effective in controlling ground settlement and structural deformation of the tunnel compared with 1.5 m-wide segments. The deformation process associated with the 1.2 m segments exhibits a more stable settlement pattern, whereas the 1.5 m segments tend to induce repeated settlement–heave cycles in the surrounding ground, with a potential risk of segmental displacement exceeding warning thresholds. Sensitivity analysis shows that different deformation indicators respond unevenly to changes in segment width. From most to least sensitive, the indicators rank as follows: maximum ground deformation, maximum displacement during the post-excavation stage, and maximum displacement during the excavation stage. The results of this study provide theoretical support and reference for selecting segmental lining width in shield tunnels constructed beneath residential buildings. Full article

Urban river floodplains function not only as zones for flood regulation and ecological buffering but have increasingly been utilized as multifunctional spaces that support leisure, waterfront, and cultural activities. However, overlapping hydraulic and geomorphic factors such as channel meandering, vegetation distribution, and flood-induced flow redistribution have amplified environmental risks, including recurrent erosion deposition, vegetation disturbance, and infrastructure damage, yet quantitative assessment frameworks remain limited. This study systematically evaluates the environmental safety of an urban floodplain by estimating vegetation variability using Sentinel-2 derived NDVI time series and deriving SEDI and TEDI through FaSTMECH two-dimensional hydraulic modeling. NDVI response cases were identified for different rainfall intensities, and interpolation-based hazard maps were generated using spatial cross-validation. Results show that the left bank exhibits higher vegetation variability, indicating strong sensitivity to hydrological fluctuations, while outer meander bends repeatedly display elevated SEDI and TEDI values, revealing concentrated structural vulnerability. Integrated analyses across rainfall conditions indicate that overall safety remains high; however, low-safety zones expand in the upstream meander and several outer bends as rainfall intensity increases. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

To quantitatively assess the storm surge induced by Super Typhoon Doksuri (2023) along the complex coastline of Fujian Province, a high-resolution Finite-Volume Coastal Ocean Model (FVCOM) was developed, driven by a refined Holland–ERA5 hybrid wind field with integrated physical corrections. The hybrid approach retains the spatiotemporal coherence of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis in the far field, while incorporating explicit inner-core adjustments for quadrant asymmetry, sea-surface-temperature dependency, and bounded decay after landfall. A series of numerical experiments were conducted, including paired tidal-only and full storm-forcing simulations, along with a systematic sensitivity ensemble in which bottom-friction parameters were perturbed and the anomalous (typhoon-related) wind component was scaled by factors ranging from 0.8 to 1.2. Static sea-level rise (SLR) scenarios (+0.3 m, +0.5 m, +1.0 m) were imposed to evaluate their influence on extreme water levels. Storm surge extremes were analyzed using a multi-scale coastal buffer framework, comparing two extreme extraction methods: element-mean followed by time-maximum, and node-maximum then assigned to elements. The model demonstrates high skill in reproducing astronomical tides (Pearson r = 0.979–0.993) and hourly water level series (Pearson r > 0.98) at key validation stations. Results indicate strong spatial heterogeneity in the sensitivity of surge levels to both bottom friction and wind intensity. While total peak water levels rise nearly linearly with SLR, the storm surge component itself exhibits a nonlinear response. The choice of extreme-extraction method significantly influences design values, with the node-based approach yielding peak values 0.8% to 4.5% higher than the cell-averaged method. These findings highlight the importance of using physically motivated adjustments to wind fields, extreme-value analysis across multiple coastal buffer scales, and uncertainty quantification in future SLR-informed coastal risk assessments. By integrating analytical, physics-based inner-core corrections with sensitivity experiments and multi-scale analysis, this study provides an enhanced framework for storm surge modeling suited to engineering and coastal management applications. Full article

Background/Objectives: Endovascular aneurysm repair (EVAR) is the standard treatment for abdominal aortic aneurysms, but the risk of endoleak compromises its effectiveness. Type IA endoleak, stemming from an inadequate proximal seal, is the most critical complication associated with the highest risk of rupture. Current preoperative planning relies on static anatomical measurements from computed tomography angiography that fail to predict seal failure due to dynamic biomechanical forces. This study aimed to retrospectively validate the predictive accuracy of a novel physics-informed digital twin and artificial intelligence (AI) model for predicting type IA endoleak risk compared to conventional static planning methods. Methods: This was a retrospective, single-center proof-of-concept validation study involving 15 patients who underwent elective EVAR (5 with confirmed type IA endoleak and 10 without type IA endoleak). A patient-specific digital twin was created for each case to simulate stent-graft deployment and capture the dynamic biomechanical interaction with the aortic wall. A logistic regression AI model processed over 16,000 biomechanical measurements to generate a single, objective metric of the endoleak risk index (ERI). The predictive performance of the ERI (using a cutoff of 0.80) was assessed and compared against a 1:3 propensity score-matched conventional control group (n = 45) who received traditional anatomical-based planning. Results: The mean ERI was significantly higher in the endoleak-positive group (0.85 ± 0.10) compared to the endoleak-negative group (0.39 ± 0.11) (p = 0.011). The digital twin/AI model demonstrated superior predictive capability, achieving an overall accuracy of 80% (95% CI: 51.9–95.7) and an area under the curve (AUC) of 0.85 (95% CI: 0.58–0.99). Crucially, the model achieved a sensitivity of 100% and a negative predictive value (NPV) of 100%, correctly identifying all high-risk cases and ruling out endoleak in all low-risk cases. In stark contrast, the matched conventional planning group achieved an overall accuracy of only 51.1% and an AUC of 0.54. Conclusion: This physics-informed digital twin and AI framework successfully validated its capability to accurately and objectively predict the risk of type IA endoleak following EVAR. The derived ERI offers a significant quantitative advantage over traditional static anatomical measurements, establishing it as a highly reliable safety tool (100% NPV) for ruling out endoleak risk. This technology represents a critical advancement toward personalized EVAR planning, enabling surgeons to proactively identify high-risk anatomies and adjust treatment strategies to minimize post-procedural complications. Further large-scale, multicenter prospective trials are necessary to confirm these findings and support clinical adoption. Full article

This study conducts a comprehensive bibliometric analysis of literature from 2000 to 2025, aiming to map the intellectual landscape and evolving trends in research on heavy metal transport within watershed ecosystems. By leveraging the Citespace literature visualization tool, we analyzed publication trends, intellectual bases, and, most importantly, the dynamic shifts in research fronts through keyword co-occurrence and clustering analysis. The findings reveal a clear trajectory from basic geochemical theories to specific applications, characterized by three prominent themes: (1) the evolution of pollution source tracking from single-method tracing to coupled multi-method quantitative modeling; (2) the establishment of a comprehensive risk evaluation chain spanning regional assessments to targeted analyses of sensitive receptors; and (3) the analysis indicates that the current research on heavy metal transport in watershed environments remains somewhat fragmented, with limited cross-comparative synthesis across different metal species and watershed contexts, and uneven progress in applying advanced data-driven and multi-model approaches. Addressing these issues is crucial for enhancing the predictive power of models and formulating effective strategies. This study thus provides a detailed overview of the field’s development while highlighting critical pathways for future research to strengthen the scientific foundation for preventing and controlling heavy metal pollution in watershed ecosystems. Full article

The long-term stability of deep underground excavations near aquifer-bearing strata is primarily controlled by the time-dependent deformation and permeability changes in the surrounding rock mass under the combined effects of mechanical loading and groundwater seepage. This study experimentally investigates the creep behavior and permeability evolution of tuff specimens subjected to stepwise reductions in confining pressure under coupled seepage and stress conditions. Conventional triaxial compression tests were conducted to determine the peak strength at confining pressures of 10, 15, and 20 MPa. Subsequently, triaxial creep tests were performed, maintaining axial stress at 70% of the previously established peak strength, with a constant seepage pressure of 4 MPa, while progressively decreasing the confining pressure. The results clearly reveal a three-stage creep process—with instantaneous, steady-state, and accelerated phases—with the radial strain exceeding axial strain and ultimately dominating at failure. This indicates that failure is characterized by significant volumetric expansion. At the specified initial confining pressures of 10 MPa, 15 MPa, and 20 MPa, the tuff specimens exhibited volumetric strains of −1.332, −1.119, and −0.836 at failure, respectively. Permeability evolution depends on the creep stage, showing a pronounced increase during the accelerated creep phase that often surpasses the cumulative permeability changes observed earlier. The specimen’s permeability at failure increased by factors of 3.97, 3.21, and 3.61 compared to the initial stage of the experiment, respectively. Additionally, permeability evolution exhibits a strong functional relationship with volumetric strain, which can be effectively modeled using an exponential function. The experimental findings further indicate that, as the confining pressure is gradually reduced, the permeability evolves following a clear exponential trend. Additionally, a higher initial confining pressure slows the rate at which permeability increases. These findings clarify the three-stage creep behavior and the associated evolution of the permeability index in tuff under coupled seepage–stress conditions. Additionally, they present a quantitative model linking permeability to volumetric strain, offering both a theoretical foundation and a new approach for assessing the long-term stability risks of deep underground engineering projects. Full article

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020–2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the highest immediate risk (95/100), while the Panama Canal is assessed as the most probable next target (90/100). State-sponsored actors from Russia, China, and Iran are responsible for most high-impact attacks. Conclusions: This research provides a validated, data-driven framework for prioritizing defensive resources. Our findings underscore the urgent need for engineering-grade solutions, including network segmentation, zero-trust architectures, and proactive threat intelligence integration to enhance maritime cyber resilience against evolving threats. Full article

Blockchain technology has rapidly evolved as a cornerstone of decentralized computing, transforming how trust, data integrity, and transparency are achieved in digital ecosystems. However, despite extensive adoption, significant gaps remain in understanding how key blockchain variables, such as block size, consensus mechanisms, and network latency, affect system vulnerabilities and susceptibility to cyberattacks. This survey addresses this gap by combining qualitative and quantitative analyses across multiple blockchain environments. Using simulation tools such as Ganache and Bitcoin Core, and reviewing peer-reviewed studies from 2016 to 2024, the research systematically maps blockchain parameters to cyberattack vectors including 51% attacks, Sybil attacks, and double-spending. Findings indicate that design choices like block size, block interval, and consensus type substantially influence resilience against attacks. The Blockchain Variable Quantitative Risk Framework (BVQRF) introduced here integrates NIST’s cybersecurity principles with quantitative scoring to assess risks. This framework represents a novel contribution by operationalizing theoretical security constructs into actionable evaluation metrics, enabling predictive modeling and adaptive risk mitigation strategies for blockchain systems. Full article

Background: Interleukin-6 (IL-6) is a key inflammatory cytokine implicated in atherosclerotic plaque progression and carotid vulnerability. Although elevated IL-6 levels have been linked to cerebrovascular risk, its prognostic value in patients undergoing carotid endarterectomy (CEA) remains undefined. This systematic review aimed to investigate the available evidence on the relationship between IL-6 levels, surgical outcomes and mechanistic evidence in CEA patients. Materials and Methods: The review followed the PRISMA statement and AMSTAR-2 critical appraisal guidelines, with the protocol registered on PROSPERO (CRD420251120023). PubMed/MEDLINE, Scopus, and Web of Science were systematically searched up to July 2025 using the terms “interleukin-6” and “carotid endarterectomy”. Original studies in humans assessing IL-6 in relation to clinical outcomes after CEA or mechanistic evidence were included without language or date restrictions. Study quality was evaluated using the Cochrane Risk of Bias 2 and NHLBI tools, and evidence certainty was appraised using the GRADE framework. Given the heterogeneity of studies, only a qualitative synthesis was performed. Results: From 1232 records identified, 13 studies encompassing 1396 patients met the inclusion criteria. Most were prospective observational cohorts, with a mean participant age of 68.52 years and 81.16% male predominance. Perioperative stroke and mortality rates were uniformly low (≤2%), consistent with contemporary registry data. Across studies, elevated IL-6 levels—whether systemic or plaque-derived—were consistently associated with symptomatic carotid disease, plaque vulnerability, and adverse long-term outcomes. However, not all studies presented quantitative data on IL-6 levels, limiting the ability to draw definitive prognostic conclusions. Conclusions: Current evidence supports a mechanistic link between IL-6–mediated inflammation and carotid plaque instability, yet robust clinical validation in surgical populations is lacking. Future large-scale, prospective studies incorporating IL-6 measurement are warranted to establish its prognostic utility, guide anti-inflammatory therapeutic strategies, and refine postoperative risk stratification in patients undergoing CEA. Full article

In 2022, Korea became the first country to introduce a climate change impact assessment (CCIA) system that requires prior analysis and evaluation of climate impacts for major development projects, delivering a relevant analysis and management framework for such purposes. This study reviews Korea’s CCIA system from a policy perspective, organizing its structural components, assessment procedures, and reporting methods according to the domains of greenhouse gas (GHG) mitigation and climate crisis adaptation. The system’s characteristics and assessment procedures of this system are also analyzed via a case study review of urban development projects. In the GHG mitigation category, emissions and absorptions should be investigated at each project stage and quantitative reduction amounts and targets established based on scientific and statistical evidence. Regarding climate crisis adaptation, regional climate risks should be analyzed and adaptation strategies for priority management areas developed based on impact prediction results. CO₂ concentrations recorded in Seoul’s central and background areas confirmed spatial differences in city-level GHG concentrations, proposing the CCIA’s potential practical use for enhancing future monitoring frameworks. To enhance the effectiveness of the CCIA and its consequences for future sustainability, the opinions of various stakeholders and linking the system with existing environmental impact (EIA) assessment frameworks are paramount. Full article

This systematic literature review uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to assess progress in blockchain-based Federated learning (FL) and Machine Learning (ML) for detecting financial fraud over the last five years (2020–2025). An initial pool of 29,274 records identified across IEEE Xplore, ACM Digital Library, and ScienceDirect yielded 1585 peer-reviewed studies that met the inclusion criteria. Both qualitative and quantitative approaches were used. The examined papers were classified according to algorithm type, fraud types, and evaluation measures. Credit card fraud and cryptocurrency fraud dominated the literature, with supervised learning (e.g., XGBoost, 95% accuracy) and federated learning (e.g., FedAvg, 91% accuracy) emerging as dominant methodologies. Centralized ML outperforms FL in latency but poses privacy risks. FL–blockchain hybrids reduce false positives. While precision, recall, and F1-score are commonly used, few studies use cost-sensitive criteria. Future research should prioritize adaptive FL aggregation, privacy-preserving ML, and cross-industry collaboration. Full article

Rear-end collisions remain a significant category of road accidents, despite widespread passive safety systems. Although modern seats are designed to reduce injury risk, the influence of accessory lumbar supports on passenger safety is still insufficiently investigated. This study analyzes the biomechanical response of a Hybrid III 50th percentile dummy on a vehicle seat fitted with various lumbar support types, compared to a reference configuration. Tests were conducted on a sled bench, simulating impacts of varying energy using crash pulses of 10 g, 15 g, and 20 g, for each tested lumbar support configuration in carefully controlled laboratory conditions. A key element of the procedure was analyzing changes in head and chest acceleration waveforms relative to results obtained for the reference seat. To quantitatively assess discrepancies between signals, the Root Mean Square Error (RMSE) and the CORA (CORrelation and Analysis) objective rating method were applied. These tools enabled precise separation of amplitude changes from phase shifts arising from different system dynamics. The results show that additional equipment elements modify dummy–seat interaction, with the extent of biomechanical response changes also depending on crash pulse value. This indicates that ergonomic supports are not biomechanically neutral and should be considered in comprehensive safety analyses. Full article

Drought remains one of the most damaging natural hazards to agricultural production and is projected to continue posing substantial risks to food security in the future, particularly in major rice-growing regions. Based on the RCP4.5 and RCP8.5 scenarios under CMIP5, this study used a process-based crop growth model to simulate the growth of rice in China in different future periods (short-term (2031–2050), medium-term (2051–2070), and long-term (2071–2090)). We fitted rice vulnerability curves to evaluate the rice drought risk quantitatively according to the simulated water stress (WS) and yield. The results showed that the drought hazard in major rice-growing areas in China (MRAC) were low in the middle and high in the north and south. The areas without rice yield loss will decline in the future, while the areas with a high yield loss will increase, especially in southwestern China and the middle and lower Yangtze Plain (MLYP). Owing to the markedly increased evaporative demand and the reduced moisture transport caused by a weakening East Asian summer monsoon, northeastern China will be a high-risk area in the future, with the expected yield loss rates in scenarios RCP4.5 and RCP8.5 being 39.75% and 45.5%, respectively. In addition, under the RCP8.5 scenario, the yield loss rate of different return periods in south China will exceed 80%. A significant gap between rice supply and demand affected by drought is expected in the short-term future. The gaps will be 67,770 kt and 78,110 kt under the RCP4.5-SSP2 and RCP8.5-SSP3 scenarios, respectively. The methodology developed in this paper can support the quantitative assessment of drought loss risk in different scenarios using crop growth models. In the context of the future expansion of Chinese grain demand, this study can serve as a reference to improve the capacity for regional drought risk prevention and ensure regional food security. Full article

Reinforced concrete structures constitute a fundamental component of modern construction; however, the execution process is highly susceptible to construction errors that may reduce the safety and durability of structural elements. Despite numerous studies addressing failures and degradation mechanisms, there is a lack of methods enabling quantitative, multi-criteria assessment of the significance of individual execution errors. The aim of this article is to identify, evaluate, and prioritize execution errors occurring during the construction of reinforced concrete structures, considering their impact on safety, durability, and repair costs. A hybrid decision-making model combining the fuzzy WINGS and fuzzy TOPSIS methods was developed to enable the assessment of execution errors under uncertainty. The scientific novelty of this study lies in the application of a hybrid fuzzy approach to the evaluation of construction errors in reinforced concrete works, allowing for the simultaneous consideration of criterion importance and the intrinsic ambiguity of expert judgments. Fuzzy WINGS was used to determine the criterion weights, while fuzzy TOPSIS facilitated the development of error rankings. Within the reinforcement-related errors, the most critical were the following: insufficient concrete cover (0.89), non-compliant reinforcement layout (0.82), and reinforcement discontinuity (0.81). Among formwork errors, the highest importance was assigned to exceeding permissible geometric deviations (0.94), while for concreting errors, the most significant were discontinuity of concreting (0.35) and improper technological joints (0.34). The proposed model provides a practical decision support tool for technical supervision, quality management, and risk assessment in reinforced concrete construction. Due to the universal structure of the hybrid fuzzy WINGS–fuzzy TOPSIS methodology itself, the approach may also be adapted in future research to other decision-making problems, should their nature justify the use of fuzzy multi-criteria methods. Full article