Search Results (3,129)

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article

The management of resectable pancreatic ductal adenocarcinoma (R-PDAC) and borderline resectable pancreatic ductal adenocarcinoma (BR-PDAC) remains a topic of active debate. Although neoadjuvant therapy (NAT) has shown clinical benefits in BR-PDAC, especially in increasing resectability and achieving higher rates of margin-negative (R0) resections, its role in R-PDAC is less clearly defined. Additionally, the role of immunotherapy in PDAC is still being explored, with ongoing trials investigating new combinations to overcome the tumor’s immune-resistant microenvironment. This article provides a comprehensive narrative review of the current evidence comparing NAT with upfront surgery in pancreatic cancer management, focusing on randomized controlled trials and meta-analyses that assess outcomes in R-PDAC and BR-PDAC. The review aims to determine whether NAT offers a significant survival advantage over traditional post-operative strategies and to clarify which clinical scenarios may benefit most from NAT. The literature was identified through a systematic search of PubMed, Scopus, and Google Scholar databases up to March 2025. Article selection adhered to the PRISMA guidelines. Our review of existing evidence supports NAT as the standard of care for BR-PDAC. Meanwhile, management of R-PDAC should be tailored individually, guided by risk stratification that considers both clinical parameters and molecular features. Immunotherapy and targeted therapies are still in early research phases, and their further integration as NAT remains controversial. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article

Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

The widespread adoption of liquefied natural gas (LNG) as a marine fuel has driven the development of LNG bunkering operations in global ports. Major international hubs, such as Shenzhen Port, have implemented ship-to-ship (STS) bunkering practices. However, this process entails unique safety risks, particularly hazards associated with vapor cloud dispersion caused by bunkering hose releases. This study employs the Phast software developed by DNV to systematically simulate LNG release scenarios during STS operations, integrating real-world meteorological data and storage conditions. The dynamic effects of transfer flow rates, release heights, and release directions on vapor cloud dispersion are quantitatively analyzed under daytime and nighttime conditions. The results demonstrate that transfer flow rate significantly regulates dispersion range, with recommendations to limit the rate below 1500 m³/h and prioritize daytime operations to mitigate risks. Release heights exceeding 10 m significantly amplify dispersion effects, particularly at night (nighttime dispersion area at a height of 20 m is 3.5 times larger than during the daytime). Optimizing release direction effectively suppresses dispersion, with vertically downward releases exhibiting minimal impact. Horizontal releases require avoidance of downwind alignment, and daytime operations are prioritized to reduce lateral dispersion risks. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

Cardiogenic shock (CS) in the setting of severe aortic stenosis (AS) presents a critical and high-risk scenario with limited therapeutic options and poor prognosis. Transcatheter aortic valve implantation (TAVI), initially reserved for inoperable or high-risk surgical candidates, is increasingly being considered in patients with CS due to improvements in device technology, operator experience, and supportive care. This review synthesizes current evidence from large registries, observational studies, and meta-analyses that support the feasibility, safety, and potential survival benefit of urgent or emergent TAVI in selected CS patients. Procedural success is high, and early intervention appears to confer improved short-term and mid-term outcomes compared to balloon aortic valvuloplasty or medical therapy alone. Critical factors influencing prognosis include lactate levels, left ventricular ejection fraction, renal function, and timing of intervention. The absence of formal guidelines, logistical constraints, and ethical concerns complicate decision-making in this unstable population. A multidisciplinary Heart Team/Shock Team approach is essential to identify appropriate candidates, manage procedural risk, and guide post-intervention care. Further studies and the development of TAVI-specific risk models in CS are anticipated to refine patient selection and therapeutic strategies. TAVI may represent a transformative option for stabilizing hemodynamics and improving outcomes in this otherwise high-mortality group. Full article

Petrochemical complexes are spatially expansive and host diverse emission sources, making accurate monitoring of volatile organic compounds (VOCs) challenging using conventional two-dimensional methods. This study introduces Mobile-extraction Differential Optical Absorption Spectroscopy (Me-DOAS), a real-time, three-dimensional remote sensing technique for assessing benzene emissions in the Ulsan petrochemical complex, South Korea. A vehicle-mounted Me-DOAS system conducted monthly measurements throughout 2024, capturing data during four daily intervals to evaluate diurnal variation. Routes included perimeter loops and grid-based transects within core industrial zones. The highest benzene concentrations were observed in February (mean: 64.28 ± 194.69 µg/m³; geometric mean: 5.13 µg/m³), with exceedances of the national annual standard (5 µg/m³) in several months. Notably, nighttime and early morning sessions showed elevated levels, suggesting contributions from nocturnal operations and meteorological conditions such as atmospheric inversion. A total of 179 exceedances (≥30 µg/m³) were identified, predominantly in zones with benzene-handling activities. Correlation analysis revealed a significant relationship between high concentrations and specific emission sources. These results demonstrate the utility of Me-DOAS in capturing spatiotemporal emission dynamics and support its application in exposure risk assessment and industrial emission control. The findings provide a robust framework for targeted management strategies and call for integration with source apportionment and dispersion modeling tools. Full article