Search Results (139)

In the dual risk model, while the ultimate ruin probability has an exact and straightforward formula, the mathematics becomes significantly more complex when considering a finite time horizon, and the literature on this topic is scarce. As a result, there is a need for numerical approximations. To address this, we develop two numerical algorithms that can accommodate a wide range of distributions for the amount of individual earnings with minimal adjustments. These algorithms are grounded in the methodologies proposed by Cardoso and Egídio dos Reis (2002) and De Vylder and Goovaerts (1988), which involve approximating the continuous risk process with a discrete-time Markov chain framework. We work out some examples, providing approximate values for the density of the time to ruin, and we compare, in the long run, our approximations with the exact values for the ultimate ruin probability to evaluate their accuracy. We also benchmark our results against the few existing figures available in the literature. Our findings suggest that the proposed approaches offer an efficient and flexible methodology for computing finite-time ruin probabilities in the dual risk model. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

The application of contagion risk spread modeling within the banking sector is a relatively recent development, emerging as a response to the persistent threat of liquidity risk that has affected financial institutions globally. Liquidity risk is recognized as one of the most destructive financial threats to banks, capable of causing severe and irreparable damage if overlooked or underestimated. This study aims to identify the most effective control strategy for managing financial contagion using a Susceptible–Infected–Recovered (SIR) epidemic model, incorporating time delays in both state and control variables. The proposed strategy seeks to maximize the number of resilient (vulnerable) banks while minimizing the number of infected institutions at risk of bankruptcy. Our goal is to formulate intervention policies that can curtail the propagation of financial contagion and mitigate associated systemic risks. Our model remains a simplification of reality. It does not account for strategic interactions between banks (e.g., panic reactions, network coordination), nor for adaptive regulatory mechanisms. The integration of these aspects will be the subject of future work. We establish the existence of an optimal control strategy and apply Pontryagin’s Maximum Principle to characterize and analyze the control dynamics. To numerically solve the control system, we employ a discretization approach based on forward and backward finite difference approximations. Despite the model’s simplifications, it captures key dynamics relevant to major European banks. Simulations performed using Python 3.12 yield significant results across three distinct scenarios. Notably, in the most severe case (${\alpha }_3=1.0$), the optimal control strategy reduces bankruptcies from 25% to nearly 0% in Spain, and from 12.5% to 0% in France and Germany, demonstrating the effectiveness of timely intervention in containing financial contagion. Full article

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. Full article

Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation. Full article

The frequent occurrence of natural disasters creates a symmetry-breaking scenario between pre-disaster planning and post-disaster rescue operations, such as post-disaster supply–demand mismatches for materials and the risk of potential facility failures. Thus, we propose a dual-uncertainty multi-scenario multi-period facility location allocation model for humanitarian rescue. The model employs two polyhedral uncertainty sets to represent facility failure risks and demand uncertainty at disaster points. Moreover, by constructing diverse disaster scenarios, it simulates material distribution schemes across different relief periods, enhancing its realism. Given that the model integrates three subproblems—facility location, supply–demand matching analysis, and emergency material allocation—we design a hybrid algorithm (DCSA-MA) that combines the discrete crow search algorithm (DCSA) and the material allocation (MA) method for its solution. Experimental results demonstrate that the model maintains a relatively high material satisfaction rate even under significant demand fluctuations. The number of facility failures has a direct bearing on emergency rescue effectiveness. The DCSA-MA method achieves a superior material satisfaction rate compared to other algorithms across various disaster scenarios and multiple rescue periods. Furthermore, DCSA-MA outperforms other algorithms in terms of solution quality, convergence, computational time, and stability. These findings indicate that DCSA-MA is an effective and highly stable approach. Full article

Cruise construction involves a lengthy logistical cycle, complex processes, and large volumes of diverse materials, inevitably generating reverse flows. To mitigate risks such as stock congestion, production disruption, and occupational hazards, this study proposes a novel reverse logistics network optimization model that integrates cost, efficiency, and Health, Safety, Environment (HSE) risk factors. Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. Fuzzy time windows quantify worker risk exposure and operational efficiency, adding decision-making complexity. A three-phase Levy mutation discrete crow search algorithm (DCSA) was developed, introducing the Levy flight strategy to replace random search and enhance the discretization and solution diversity. The comparative analysis shows that DCSA performs as well as NSGA-II, while outperforming DGWO, demonstrating both stability and efficiency. Comparative analysis with a cost-only scenario revealed that although short-term economic gains may be achieved under cost minimization, such approaches often overlook risks with potential long-term impacts. This highlights the necessity of integrating safety concerns into reverse logistics planning, and confirms the model’s robustness and practical value, thus supporting decision makers in aligning reverse logistics planning in shipyards with sustainability and operational efficiency goals. Full article

This manuscript proposes a methodology to leverage digitalisation to efficiently generate an overview of required condition-based railway track interventions, possession windows, and expected costs for railway networks at the beginning of the intervention planning process. The consistent and efficient generation of such an overview not only helps track managers in their decision-making but also facilitates the discussion among other decision-makers in later phases of the track intervention planning process, including line planners, capacity managers, and project managers. The methodology uses data of different levels of detail, discrete state modelling for uncertain deterioration of components, and component-level intervention strategies. It dynamically updates the condition estimates of components by capturing the interaction between deteriorating components using Bayesian filters. It also estimates the risks associated with different types of potential service losses that may occur due to sudden events using fault trees as a function of time and the condition of components. An implementation of the methodology is conducted for a 25 km regional railway network in Switzerland. The results suggest that the methodology has the potential to help track managers early in the intervention planning process. In addition, it is argued that the methodology will lead to improvements in the efficiency of the planning process, improvements in the scheduling of preventive interventions, and the reduction in corrective intervention costs upon the implementation in a digital environment. Full article

This study presents an elastic one-dimensional numerical model to simulate the filling process of a large-scale, partially drained pipeline with an undulating profile, incorporating bypass systems. The model uses the Method of Characteristics to solve water hammer equations and integrates the Discrete Gas Cavity Model to capture column separation effects. Validation is performed using two experimental test rigs and comparisons with existing numerical models, showing RMSE values between 1.06 and 7.95. The results highlight three key findings: (1) oversized bypasses generate severe transient pressures; (2) effective air management enables higher filling flow rates, significantly reducing filling time; and (3) bypass lines help dampen pressure fluctuations, with a notable drop in $∆H$ from 528 m to 6.8 m occurring in stage b, following the release of trapped air. Additionally, this study challenges the practicality of the AWWA’s recommended pipeline filling velocity limit of 0.3 m/s, showing that strict adherence to this guideline is often unrealistic for large-scale systems. Overall, the findings emphasize the need for a balanced design approach that reduces transient risks while maintaining operational efficiency in large-scale pipelines. Full article

This work develops a practical multi-period optimization approach that incorporates real-world constraints, including discrete decisions and conic risk constraints. Expanding upon earlier single-period models, our framework employs a binary scenario tree derived from monthly returns of randomly selected S&P 500 stocks to represent market evolution across multiple periods. The formulation captures essential portfolio constraints, such as transaction fees, sector diversification, and minimum investment thresholds, resulting in a robust and comprehensive optimization approach. To efficiently solve the resulting mixed-integer second-order cone programming (MISOCP) problem, we employ an outer approximation algorithm with a warmstart strategy, which significantly improves solution runtimes and computational efficiency. Numerical experiments demonstrate the model’s effectiveness, showing an average improvement of $10.71\%$ in iteration count and $15.24\%$ in computational time when using the warmstart approach. Full article

We study the use of a homogeneous fleet of drones to design an unattended persistent drone-based patrolling system for vast circular areas. The drones follow flight missions supported by auxiliary on-ground charging stations, whose location and number must be determined. To this end, we first present a mixed integer non-linear programming model for defining cyclic schedules of drone flights considering the selection of the drone model from a set of candidate drone platforms. By imposing a minimum acceptable time between consecutive visits to any perimeter point, the objective consists of minimizing the total surveillance system deployment cost. The solution provides the best platform, the location of base stations, and the number of drones needed to monitor the perimeter, as well as the flight mission for each drone. We test five commercial platforms in six different scenarios whose radios vary between 1196 and 1696 m. In five of them, the MD4-100 Microdrones model achieves the lower cost solution, with values of EUR 66,800 and 83,500 for Scenarios 1 and 2 and EUR 116,900 for Scenarios 3, 4 and 5, improving its rivals in average percentages that vary between 8.46% and 70.40%. In Scenario number 6, the lower cost solution is provided by the TARTOT-500 model, with a total cost of EUR 168,000, improving by 20% the solution provided by the MD4-100. After obtaining the optimal solution, to evaluate the system robustness, we propose a discrete event simulation model incorporating uncertain flight times taking into account the possibility of accelerated depletion of drones’ Lithium-Ion polymer (Li-Po) batteries. Overall, our research investigates how various factors—such as the number of drones in the fleet and the division of the perimeter into sectors—impact the reliability of the system. Using Scenario number 3, our tests demonstrate that under a risk of battery failures of 2.5% and three UAVs per station, the surveillance system reaches a global percentage of punctually patrolled sectors of 92.6% and limits the number of delayed sectors (the relay UAV reaches the perimeter slightly above the required time, but it positively re-establishes the cyclic pattern for patrolling) to only a 5.6%. Our findings provide valuable insights for designing more robust and cost-effective drone patrol systems capable of operating autonomously over large planning horizons. Full article

The analysis in this study covers how power imbalance, alliance cohesion, diplomatic and media framing, and big data analytics affect scaling up in the conflict in a multipolar world. This research applies the Constructivist International Relations Theory to examine survey data of 250 international relations experts, policymakers, and analysts using Survey Structured Equation Modeling (SEM) via SMART-PLS. Power imbalance and the way the media frames the situation are found to lead to an escalation of conflicts, but strong alliance cohesion, diplomatic effort, and big data analytics can mitigate the risk of the escalation. Strategic diplomacy, media regulation, and real-time data monitoring have thus shown their capacity to prevent conflict. These contribute to conflict studies by incorporating political IR models, data science knowledge, and policy advice on global security governance. This means they can support the prediction and prevention of conflicts by means of diplomatic transparency, ethical media practice, and AI early warning systems. This study is limited by the use of self-reported data; however, the results of this study indicate that this topic is under-explored in cultural and geopolitical terms. The results help inform policymakers and security entities on ways to address conflict resolution as a matter of discretion and from a multidimensional perspective. Survey Structured Equation Modeling (SEM) via SMART-PLS is a technique used for analyzing structural relationships between measured variables and latent constructs, providing valuable insights into complex models. Survey Structured Equation Modeling (SEM) via SMART-PLS is a technique used for analyzing structural relationships between measured variables and latent constructs, providing valuable insights into complex models. Full article

Cracks are a common early road defect that tends to worsen with the aging of roads, potentially leading to severe structural damage. Timely and accurate crack detection plays a crucial role in mitigating such risks and holds significant importance for infrastructure maintenance. Deep learning techniques have demonstrated excellent performance in image-based crack extraction tasks. However, challenges persist due to the presence of numerous noisy pixels in the image background and the diverse and intricate morphologies of cracks, leading to issues such as misclassification and omission. To address these issues, this paper proposes a refined pixel-level segmentation network (ANF-Net) suitable for complex crack detection scenarios with high noise levels and diverse crack morphologies. When extracting crack features, on one hand, the network introduces an attention module tailored for crack scenes to learn pixel-wise feature weights, enabling the network to focus on crack regions and thereby reducing the impact of similar background features, mitigating false positives caused by noise misclassification. On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. This design enhances the network’s ability to extract cracks in morphologically diverse, narrow, and densely populated regions, effectively preventing issues such as crack extraction interruptions and omissions. Additionally, a multi-scale discrete wavelet transform enhancement module is designed to assist the network in considering frequency domain information that contains crack features, further improving its feature extraction capability. Simulations are conducted using three publicly available crack datasets, and the proposed method is compared with mainstream segmentation models. The results demonstrate that the proposed method achieves F1 scores of 87.9%, 82.5%, and 71.5% on the three datasets, respectively, all of which surpass the performance of current mainstream segmentation models. The proposed network accurately extracts road cracks and exhibits robust performance. Full article

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this paper proposes a dynamic risk-modeling method based on Stochastic Petri Nets (SPN) and Bayesian theory to deeply explore the evolution mechanism of urban natural fires. The SPN model is constructed through natural language processing techniques, which discretize the accident evolution process. Then, the Bayesian theory is introduced to dynamically update the model parameters, enabling the accurate assessment of key event nodes. The research results show that this method can effectively identify high-risk nodes in the evolution of fires. Their dynamic probabilities increase significantly over time, and key transition nodes have a remarkable impact on the emergency response efficiency. This method can increase the fire prevention and control efficiency by approximately 30% and reduce potential losses by more than 20%. The dynamic update mechanism significantly improves the accuracy of risk prediction by integrating real-time observation data and provides quantitative support for emergency decision making. It is recommended that urban management departments focus on strengthening the maintenance of facilities in high-risk areas (such as fire alarm systems and emergency passages), optimize cross-departmental cooperation processes, and build an intelligent monitoring and early-warning system to shorten the emergency response time. This study provides a new theoretical tool for urban fire risk management. In the future, it can be extended to other types of disasters to enhance the universality of the model. Full article