Search Results (245)

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

This study presents a simulation-based damage modeling and fatigue risk assessment of a reusable ceramic matrix composite thruster designed for short-duration, green bipropellant propulsion systems. The thruster is constructed from a fiber-reinforced ultra-high temperature ceramic matrix composite composed of zirconium diboride, silicon carbide, and carbon fibers. Time-resolved thermal and structural simulations are conducted on a validated thruster geometry to characterize the severity of early-stage thermal shock, stress buildup, and potential degradation pathways. Unlike traditional fatigue studies that rely on empirical fatigue constants or Paris-law-based crack-growth models, this work introduces a simulation-derived stress-margin envelope methodology that incorporates ±20% variability in temperature-dependent material strength, offering a physically grounded yet conservative risk estimate. From this, a normalized risk index is derived to evaluate the likelihood of damage initiation in critical regions over the 0–10 s firing window. The results indicate that the convergent throat region experiences a peak thermal gradient rate of approximately 380 K/s, with the normalized thermal shock index exceeding 43. Stress margins in this region collapse by 2.3 s, while margin loss in the flange curvature appears near 8 s. These findings are mapped into green, yellow, and red risk bands to classify operational safety zones. All the results assume no active cooling, representing conservative operating limits. If regenerative or ablative cooling is implemented, these margins would improve significantly. The framework established here enables a transparent, reproducible methodology for evaluating lifetime safety in ceramic propulsion nozzles and serves as a foundational tool for fatigue-resilient component design in green space engines. Full article

Housing sustainability is a cornerstone element of sustainable economic and social development. This is particularly true for China, where high-rise residential buildings are the primary form of housing. In recent years, China has experienced frequent housing-related accidents, resulting in a significant loss of life and property damage. This study aims to identify the key factors influencing housing sustainability and provide a basis for the prevention and control of housing-related safety risks. This study has developed a housing sustainability evaluation indicator system comprising three primary indicators and 16 secondary indicators. This system is based on an analysis of the causes of over 500 typical housing accidents that occurred in China over the past 10 years, employing research methods such as literature reviews and expert consultations, and drawing on the analytical frameworks of risk management theory and system safety theory. Subsequently, the NK-SNA model, which significantly outperforms traditional models in terms of adaptive learning and optimization, as well as the explicit modeling of complex nonlinear relationships, was used to identify the key risk factors affecting housing sustainability. The empirical results indicate that the risk coupling value is correlated with the number of risk coupling factors; the greater the number of risk coupling factors, the larger the coupling value. Human misconduct is prone to forming two-factor risk coupling with housing, and the physical risk factors are prone to coupling with other factors. The environmental factors easily trigger ‘physical–environmental’ two-factor risk coupling. The key factors influencing housing sustainability are poor supervision, building facilities, the main structure, the housing height, foundation settlement, and natural disasters. On this basis, recommendations are made to make full use of modern information technologies such as the Internet of Things, big data, and artificial intelligence to strengthen the supervision of housing safety and avoid multi-factor coupling, and to improve upon early warnings of natural disasters and the design of emergency response programs to control the coupling between physical and environmental factors. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior. Full article

Fast charging accelerates lithium-ion battery operation but increases the risk of lithium (Li) plating—a process that undermines efficiency, longevity, and safety. Here, we introduce a predictive modeling framework that captures the onset and severity of Li plating under practical fast-charging conditions. By integrating an empirically parameterized SOC threshold model with time-dependent kinetic simulations and Arrhenius based thermal analysis, we delineate operating regimes prone to irreversible Li accumulation. The framework distinguishes reversible and irreversible plating fractions, quantifies energy losses, and identifies a critical activation energy (0.25 eV) associated with surface-limited deposition. Visualizations in the form of severity maps and voltage-zone risk classifications enable direct application to battery management systems. This approach bridges electrochemical degradation modeling with real-time charge protocol design, offering a practical tool for safe, high-performance battery operation. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

This study evaluates the current Spanish insurance framework for catastrophic flood risk, administered by the Consorcio de Compensación de Seguros (CCS), based on nationwide loss data reported by the CCS for the period 1996–2020. The analysis of historical claims data enables a clear differentiation between frequent, low-cost events and infrequent, high-impact catastrophes. While the CCS has fulfilled a critical role in post-disaster compensation, the findings highlight the parallel need for ex ante risk mitigation strategies. The study proposes a more efficient, two-tier risk coverage model. Events whose impacts can be managed through standard insurance mechanisms should be underwritten by private insurers using actuarially fair premiums. In contrast, events with catastrophic implications—due to their scale or financial impact—should be addressed through general solidarity mechanisms, centrally managed by the CCS. Such a risk segmentation would improve the financial sustainability of the system and create fiscal space for prevention-oriented incentives. The current design of the CCS scheme may generate moral hazard, as flood exposure is not explicitly priced into the premium structure. Empirical findings support a shift towards a more transparent, incentive-aligned model that combines collective risk sharing with individual risk responsibility—an essential balance for effective climate adaptation and long-term resilience. Full article

Transcatheter aortic valve replacement (TAVR) is a high-risk cardiovascular interventional procedure with a high incidence of postoperative complications, urgently requiring more refined risk identification and mitigation strategies. The main challenges in assessing the risk of TAVR complications lie in the scarcity of real-world data and the co-occurrence of multiple complications. This study developed an adjustment evaluation model that adapts randomised clinical trial (RCT) evidence to real-world data (RWD) and adopted multi-label classification methods that incorporate a LocalGLMnet-like regularization term, enabling data-adaptive parameter shrinkage for more accurate estimation. In the empirical analysis, with real surgical data from a hospital in the United States, a combination of multi-label random sampling and representative multi-label classification algorithms was used to fit the data. The model was compared across multiple evaluation metrics, including Hamming loss, ranking loss, and micro-AUC, to ensure robust results. The model used in this paper bridges the gap between medical risk prediction and insurance actuarial science, provides a practical data modelling foundation and algorithmic support for the future development of post-operative complication insurance products that precisely align with clinical risk. Full article

Questionnaire data plays a key role in social research, especially when evaluating public attitudes using Likert-type scales. Yet, traditional analyses often merge some ordinal categories and exclude responses such as Don’t Know, No Answer, or Refused—risking the loss of valuable information. This study introduces BS-TOSIE (Belief Structure-Based TOPSIS for Survey Item Evaluation), a novel method that preserves and integrates all response types, including ambiguous ones. By combining the Belief Structure framework with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, BS-TOSIE offers a structured approach to ranking and evaluating individual survey items measured on an ordinal scale, even in the presence of missing or incomplete data. Response distributions are transformed into a belief structure vector, enabling comparison against ideal and anti-ideal benchmarks. We demonstrate this approach using data from the Quality of Life in European Cities survey to assess perceptions of local governance in European cities. This study analyzes changes in citizen satisfaction with local public administration across five key dimensions—timeliness, procedural clarity, fairness of fees, digital accessibility, and perceived corruption—in 83 European cities between 2019 and 2023. The findings reveal persistent regional disparities, with Northern and Western European cities consistently outperforming those in Southern and Eastern Europe, although some cities in Central Europe show signs of improvement. Zurich consistently received high satisfaction scores, while other cities, such as Rome and Palermo, showed lower scores. Unlike traditional methods, our approach preserves the full spectrum of responses, yielding more nuanced and interpretable insights. The results show that BS-TOSIE enhances both the clarity and depth of survey analysis, making a methodological contribution to the evaluation of ordinal data and offering empirical insights into public perceptions of local city administration. Full article

Background: Finasteride, a 5α-reductase inhibitor, is used for androgenetic alopecia and benign prostatic hyperplasia. However, concerns have emerged about its psychiatric side effects, including suicidality. This study analyzed finasteride-related reports from the FDA Adverse Event Reporting System (FAERS) to identify potential safety signals. Methods: Adverse events reported from 2015 to 2024 were extracted using preferred terms, quantified using Bayesian analysis and disproportionality metrics, including empirical Bayesian geometric mean (EBGM), information component (IC), reporting odds ratio (ROR), and proportional reporting ratio (PRR). Results: Most were male (87%), with 43% aged 18–40 years, primarily using finasteride for hair loss. Disproportionality metrics for suicidality-related events fluctuated between 2019 and 2024. In 2019, the ROR was 27.51 (95% CI: 23.22–32.58), the PRR was 21.96 (95% CI: 18.54–26.01), the EBGM was 20.50, and the IC was 4.36. A slight decline was observed in 2020, a surge in 2021, and a peak in 2022 (ROR 34.64 (95% CI: 28.36–41.88), PRR 27.82 (95% CI: 22.30–34.61), EBGM 24.96, IC 4.64). Although a sharp rise in suicidality reports was noted in 2024, the rates of ROR and PRR dropped to 19.04 (95% CI: 17.02–21.30) and 16.53 (95% CI: 14.78–18.50), respectively. Serious outcomes such as disability (18.7%), life-threatening events (12.9%), and death (7.5%) were also noted. Conclusions: The upward trend in suicidality-related safety signals among young male users since 2019, which peaked in 2024, reflects emerging safety concerns among finasteride users, reinforcing the need for pharmacovigilance. Collaborative action among healthcare professionals, regulatory authorities, and pharmaceutical companies, along with clear warnings and mental health assessments before and throughout finasteride therapy, can mitigate potential psychiatric risks and enhance patient safety. Full article

Foot and Mouth Disease (FMD) poses significant challenges to livestock management and agricultural economies worldwide. This study examines the effect of farmers’ sociodemographic factors on livestock infected with Foot and Mouth Disease (FMD) and analyzes its socioeconomic impact on smallholder farmers in Indonesia. This study collected data from 992 households (202 infected and 790 non-infected) in the special region of Yogyakarta province. The research used propensity score matching (PSM) treatment effect analysis to assess the socioeconomic impact of FMD outbreaks on smallholder farmers. Our results demonstrated that FMD significantly increased (p < 0.01) smallholder farmers’ social behavior, including knowledge, attitude, and practice (KAP). Furthermore, farmers whose animals are already infected with FMD must spend an additional IDR 258,000 to IDR 270,000 on treatment compared to non-infected ones. This study provides empirical evidence that farmer characteristics, including women’s decision-making, income, farming group, and cattle ownership, determine the likelihood of FMD infection, which implies that farmers with specific characteristics may heighten the risk of FMD infection. We concluded that FMD has changed social behavior and accelerated economic loss for smallholder farmers. Hence, farmers with animals at risk of FMD infection are prioritized in FMD control programs. Full article

The changing climate poses a significant challenge to the dairy industry, particularly in Punjab’s Faisalabad region, a central milk production hub. Rising temperatures and humidity exacerbate heat stress, endangering rural livelihoods. This study quantifies the impacts of these climatic stressors on milk yield, evaluates smallholder farmers’ perceptions of climate risks, and projects future losses to guide adaptive policymaking. By integrating Likert-scale surveys of 450 dairy farmers with advanced panel regression models (including fixed and random effects) and a dynamic panel generalized method of moments (GMM) approach for forecasting, we analyzed eight years of milk production and climate data (2017–2024) under IPCC scenarios (+2 °C, +10% humidity). The results revealed significant declines: a 1 °C temperature increase reduced milk yields by 1.72 L per month (p < 0.01), while a 1% rise in humidity decreased output by 0.59 L per month (p < 0.01). Compounded losses under combined stressors reached 2.25 L per month, with hotter regions (Faisalabad’s semi-arid zone) experiencing the steepest declines. Farmers’ perceptions are closely aligned with empirical trends, identifying heat humidity interactions as the most critical risks. To mitigate these losses, adaptive strategies such as heat-resistant cattle breeds, humidity-responsive cooling systems, and targeted financial support for smallholders are critical. This study connects farmers’ insights with econometric modeling to provide practical strategies to enhance resilience in Punjab’s dairy sector. Full article

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient $p$. In the special case $p=1$, the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition $p>0$, although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article

A field study on the biparental convict cichlids (Amatitlania siquia) in Lake Xiloá, Nicaragua was conducted to understand how the loss of a parent’s parental care affects the antipredator behavior of both parents and offspring during intruder events. We hypothesized that the combined efforts of two parents would result in increased intruder aggression and decreased offspring dispersion compared to single-parents of either sex, and that single-females and males would differ in their ability to deter predators and manage offspring dispersion. Both parents in a pair chased half the intruders that single-females did and the same number as single-males, suggesting that the presence of a partner deters intruders from encroaching and affords parents more time to engage in other parental care duties. Compared to single-parents, offspring accompanied by both parents were seldom left alone and showed greater shoal cohesion—both of which would presumably lower their risk of predation. Although there were sex differences between single-parents in terms of how often they left their offspring unattended and called to them using pelvic fin-flicks, neither sex was found to be more effective at managing the distribution of their offspring. This field study provides empirical evidence to support the need for biparental care in this species and gives insight into the selection pressures shaping parental investment. Full article