Search Results (548)

This paper examines how assets from emerging and developed stock markets can be efficiently allocated during periods of financial crisis by integrating traditional portfolio theory with Extreme Value Theory (EVT), using the Generalized Pareto Distribution (GPD) and Generalized Extreme Value (GEV) approaches to model tail risks. This study evaluates mean-variance portfolios constructed under each EVT framework and finds that portfolios based on GPD estimates consistently favour emerging market assets, which outperform both developed market and internationally diversified portfolios during extreme market conditions. In contrast, GEV-based portfolios indicate superior performance for developed market assets, highlighting the distinct behaviour of returns in the upper and lower tails of the distribution. These contrasting results reveal the unique nature of safe-haven characteristics associated with developed economies, the assets of which demonstrate greater stability and resilience during episodes of financial stress. By showing how tail-risk modelling alters optimal portfolio weights across market types, this paper contributes new evidence to the literature on crisis-informed asset allocation and offers practical insights for investors seeking robust diversification strategies under extreme market fluctuations. Full article

Reliable estimation of extreme rainfall is essential for hydraulic design and flood risk mitigation, particularly in arid regions where rainfall exhibits strong temporal and spatial variability. This study presents a statistical framework for developing rainfall intensity-duration-frequency (IDF) curves, complemented by a machine learning-based assessment of model bias and performance. The analysis was conducted using data from ten rainfall stations located within or near the Wadi Al-Rummah Basin. Annual maximum series (AMS) from 1969 to 2024 were first reconstructed to address missing years using a modified normal ratio method (NRM) combined with nearest-station selection, ensuring spatial consistency while preserving station-specific rainfall characteristics. Six probability distributions (Weibull, Gumbel, gamma, lognormal, generalized extreme value (GEV), and generalized Pareto) were fitted to each station, and the best-fit distribution was identified using multiple goodness-of-fit (GOF) criteria, including the Kolmogorov–Smirnov (K-S) test, Anderson–Darling (A-D) test, root mean square error (RMSE), chi-square (χ²) statistic, Akaike information criterion (AIC), Bayesian information criterion (BIC), and the coefficient of determination (R²). Statistical IDF curves were then developed for durations ranging from 5 to 1440 min and return periods from 2 to 1000 years. To evaluate the robustness of the statistically derived IDF curves, three machine learning (ML) models, multiple linear regression (MLR), regression random forest (RRF), and multilayer feed-forward neural network (MFFNN), were trained as surrogate models using duration, return period, and station geographic attributes as predictor variables. Model performance was evaluated using RMSE, MAE, and mean bias metrics across stations and return periods. The lognormal distribution emerged as the best-fit model for four stations, while the Gumbel and gamma distributions were selected for two stations each. Overall, no single probability distribution consistently outperformed others, indicating station-dependent behavior. Among the machine learning models, the MFFNN achieved the closest agreement with statistical IDF estimates ($RMSE\approx 0.97$, $MAE\approx 0.65$, $bias\approx -0.02$), followed by RRF and MLR based on global average performance across all stations and return periods. The proposed framework offers a reliable approach for rainfall IDF development and evaluation in arid region watersheds. Full article

Landslides, often triggered by intense or prolonged rainfall, pose significant risks to communities and infrastructure. Identifying accurate rainfall thresholds is crucial for predicting landslide events and developing effective early warning systems. This study, conducted on São Miguel Island (Azores), aimed to improve the predictive capability of rainfall thresholds by integrating both rainfall preparatory and rainfall trigger thresholds. Using data from 61 landslide events and rainfall measurements recorded at four stations between 1977 and 2020, the study applied the Generalised Extreme Value (GEV) distribution with Maximum Likelihood Estimation (MLE) to identify the cumulative rainfall–duration pair with the highest return period for each event, thereby establishing a preparatory threshold. The trigger threshold was determined by analysing the rainfall amount recorded on the day of the event while also accounting for the duration of the preparatory rainfall period. The final threshold combines both the preparatory and trigger thresholds, and an event is detected when both thresholds are exceeded. Preparatory thresholds showed similar patterns across the stations, with Sete Cidades and Furnas recording the highest cumulative rainfall values, while Santana and Ponta Delgada exhibited lower thresholds. The trigger thresholds at Furnas reflected the highest daily rainfall intensities. The analysis also indicated that the rainfall intensity required to trigger landslides decreases with increasing durations of the antecedent rainfall. Performance of the thresholds using ROC metrics revealed that the combined threshold outperformed the preparatory threshold alone by reducing false positives (FPs) and improving predictive accuracy. In all cases, the combined threshold demonstrated superior performance in detecting landslide events, highlighting its effectiveness in landslide prediction. This study provides a detailed analysis of rainfall thresholds for landslides on São Miguel Island and underscores the advantages of the combined threshold approach for improving landslide prediction and supporting the development of robust early warning systems. Full article

Climate change has resulted in frequent extreme weather events such as heavy rainfall and heat waves in Japan, making accurate forecasting and countermeasures an urgent issue. Therefore, it is urgently required to analyze the statistical characteristics of extreme rainfall events using the extreme value theory (EVT). The generalized extreme value (GEV) distribution, a core model for EVT, was applied in this study to rainfall data collected in Kakunodate, Akita Prefecture, Japan, spanning May 1976 to December 2023. The analysis results confirm the presence of extreme rainfall events. Through model fitting, the GEV parameters representing location, scale, and shape were accurately estimated. The model demonstrated a good fit, particularly for moderate-intensity rainfall. However, notable uncertainties emerged in the prediction of the most extreme events. Return period analysis results indicated that extreme rainfall events occur at intervals ranging from 2 to 100 years, suggesting the necessity of incorporating safety margins into long-term forecasting frameworks. Considering the increasing frequency of such events, cross-validation with alternative statistical methods and the potential adoption of non-smooth GEV models are recommended to enhance predictive reliability. Overall, the results of this study highlight the need for adaptive and flexible revisions to infrastructure design criteria in response to evolving patterns of extreme weather. Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article

We investigate the modifications to inflationary observables that arise when adopting an $\alpha$-vacuum instead of the standard Bunch–Davies vacuum for quantum fluctuations during inflation. Within the Starobinsky inflationary model, we compute and compare the scalar spectral index, its running, and the running of the running arising from different choices of the initial vacuum state. We further examine the energy scales associated with $\alpha$-vacua and argue that, for any number of extra spatial dimensions, the relevant scale can be truncated at the Hubble scale, ∼$\mathcal{O}\left({10}^{13}\right)\mathrm{GeV}$, without conflict with current Cavendish-type experimental bounds on sub-millimeter gravity (∼$250\mathsf{\mu }\mathrm{m}$). Our analysis demonstrates that the $\alpha$-vacuum is subject to stringent constraints as a viable de Sitter-invariant alternative to the Euclidean (Bunch–Davies) vacuum, with the corrections that it induces in the inflationary observables being strongly limited by the latest Planck data. Full article

In this study, we extract a 7-day binned $\gamma$-ray light curve from 2008 August to 2019 March in the energy range 0.1–300 GeV and identify four outburst periods with peak flux of >8.0$\times {10}^{-7}$ ph ${\mathrm{cm}}^{-2}{\mathrm{s}}^{-1}$. Four active states in the optical are also marked during this period. The fastest variability timescale suggests the emission region radius is R ∼ 2.4$\times {10}^{16}$ cm, and the observed emission region lies within <0.7 pc distance from the central engine. The majority of short-timescale flares exhibit a symmetric temporal profile, implying that the rise and decay timescales are dominated by disturbances caused by dense plasma blobs passing through the standing shock front in the jet region. To understand the properties of the source jets, we employ a standard one-zone leptonic scenario to model the broadband spectral energy distributions (SEDs) of flaring periods and determine that the $\gamma$-ray spectrum is better reproduced when the dissipation region of the jet is located within the molecular torus (MT). The $\gamma$-ray spectra from the outburst phases show an obvious spectral break with a break energy between 3.00 and 7.08 GeV, which may be attributed to an intrinsic break in the energy distribution of radiating particles. The studies of the survival time of a sheet before being destroyed by the turbulent motions of plasma (${\tau }_{cs}\sim 2.9\times {10}^4$ s), the shock acceleration time ($t_{acc}\sim$$4.3\times {10}^4$ s), and the minimum interaction height ($Z_{min}$ ≈ 2.57–4.55$\times {10}^{17}$ cm > $R_{BLR}$ ∼ 1.0$\times {10}^{17}$ cm) suggest that the $\gamma$-ray flaring event maybe caused by a magnetic reconnection mechanism, but we cannot completely rule out the shock-in-jet model. Full article

Trees play a critical role in urban ecological protection and wind disaster mitigation, yet conventional Gaussian-based wind engineering models often underestimate extreme tree motions under turbulent flows. This study aims to clarify the statistical characteristics of tree wind-induced responses and develop a quantitative framework to distinguish Gaussian and non-Gaussian behaviors. Scaled aeroelastic tree models were tested in a boundary-layer wind tunnel under controlled turbulence intensity (0.05–0.19), mean wind speeds of 3.9–9.3 m/s, and leaf area index (LAI) of 0–2.46. Acceleration and displacement time histories of branches, crown center, and trunk were recorded. A Gaussian discrimination criterion was established using cumulative probability thresholds of skewness and kurtosis, supplemented by time-history and probability density verification. Results reveal that branch accelerations exhibit strong non-Gaussianity with heavy-tailed and asymmetric distributions, crown displacements show moderate non-Gaussianity, while trunk responses remain near-Gaussian due to higher stiffness. Under weak turbulence, Gamma and Lognormal distributions fit best; under strong turbulence, the Generalized Extreme Value (GEV) distribution prevails. A high-quantile GEV-based framework markedly reduces extreme response prediction bias compared with Gaussian assumptions. These findings provide a probabilistic basis for more accurate assessment of tree wind stability and the design of wind-resistant urban vegetation and shelterbelts. Full article

In finance, accurately modelling the tail behaviour of extreme log returns is critical for understanding and mitigating risks across diverse asset classes. This research employs extreme value theory to identify the most suitable probability distributions (i.e., generalized extreme value (GEV), generalized logistic (GLO), Gumbel (GUM), generalized Pareto (GP), and reverse Gumbel (REV)) and estimation methods (least squares (LS), weighted least squares (WLS), maximum likelihood (ML), L-moments (LM), and relative least squares (RLS)) for modelling the tail behaviour of log returns from two financial datasets, each representing a distinct asset class with high (Ethereum, a digital asset class) and low (South African government bonds, a fixed-income asset class) volatility levels. The performance of each model and estimation method (25 different possibilities) is evaluated through goodness-of-fit and risk measures as the study aims to determine the optimal approach for each volatility level. Results from ranking different models and estimation methods show that across both asset classes, ML consistently emerges as the top-performing estimation method across all distributions. LM serves as a solid secondary option, while LS occasionally excels under GLO’s weekly minima for low volatility, whereas RLS occasionally surpasses ML in GLO’s monthly minima for high volatility. Finally, WLS uniquely outperforms under GEV and GLO’s monthly minima under low volatility. Full article

An extraordinary episode is a torrential rainfall event that produces significant societal impacts, which poses a major natural hazard in the western Mediterranean, particularly along the Valencia coast. This study evaluates the feasibility and added value of an explicitly spatial approach for estimating return periods of extraordinary precipitation in the Júcar and Turia basins, moving beyond traditional point-based or micro-catchment analyses. Our methodology consists of progressive spatial aggregation of time series within a basin to better estimate return periods of exceeding specific catastrophic rainfall thresholds. This technique allows us to compare 10 min rainfall data of a reference station (e.g., Turís, València, 29 October 2024 catastrophe) with long-term annual maxima from 98 stations. Temporal structure is characterized using the fractal–intermittency n-index, while tail behavior is modeled using several extreme-value distributions (Gumbel, GEV, Weibull, Gamma, and Pareto) and guided by empirical errors. Results show that $n\approx 0.3$–0.4 is consistent for extreme rainfall, while return periods systematically decrease as stations are added, stabilizing with about 15–20 stations, once the relevant spatial heterogeneity is sampled. Specifically, the probability of exceeding extraordinary thresholds is between 3 and 10 times higher for the areal than the point approach, so recurrence of a catastrophe would be once a few decades rather than centuries. Overall, the results demonstrate that spatially integrated return-period estimation is operational, physically consistent, and better suited for basin-scale risk assessment than purely point-based approaches, providing a relevant baseline for interpreting recent catastrophic events in the context of ongoing climatic warming in the Mediterranean region. Full article

Fragility curves are essential for assessing the vulnerability of buildings to earthquake-induced damage, representing the probability of exceeding various damage states as a function of seismic intensity. They enable rapid pre-screening of large building stocks, guiding focused analyses, monitoring, and mitigation strategies. This study introduces an empirical approach using the Generalized Extreme Value (GEV) distribution to model fragility curves, offering greater flexibility than the conventional lognormal method. In this work, GEV-based curves were derived from empirical data retrieved from the Italian Da.D.O. platform using Python 3.10.1 tool. The approach provides a practical framework for accurate, large-scale seismic risk assessment. Full article

This paper demonstrates seasonal variations in the generalised extreme value (GEV) distribution shape parameter and discrepancies in GEV types within the same location. Daily rainfall data from 26 rain gauge stations located in Tasmania were used as a case study. Four GEV distribution parameter estimation techniques, such as MLE, GMLE, Bayesian, and L-moments, were used to determine the shape parameter of the distribution. With the estimated shape parameter, the spatial variations under different seasons were investigated through GIS interpolation maps. As there is strong evidence that shape parameters potentially vary across locations, spatial analysis focusing on the shape parameter across Tasmania (Australia) was performed. The outcomes of the analysis revealed that the shape parameters exhibit their highest and lowest values in winter, with a range from −0.234 to 0.529. The analysis of the rainfall data revealed that there is significant variation in the shape parameters among the seasons. The magnitude of the shape parameter decreases with elevation, and a non-linear relationship exists between these two parameters. This study extends knowledge on the current framework of GEV distribution shape parameter estimation techniques at the regional scale, enabling the adoption of appropriate GEV types and, thus, the appropriate determination of design rainfall to reduce hazards and protect our environments. Full article

The aim of this paper is to present a more complete analysis of the theoretical concepts and experimental aspects of the physics of photoproduction interactions involving nuclei. We thus determine the relative contributions of excited nucleon, $p\pi$, and $p\pi \pi$ resonances and $\rho$, $\eta$, $\omega$ and K production, as well as the subsequent decay channels leading to neutrino and $\gamma$-ray production. This treatment is based, in large part, on the most recent and extensive empirical data on particle photoproduction interactions off protons and He nuclei. It is shown that, in astrophysical sources with steep proton energy spectra, the $\Delta$(1232) resonance channel clearly dominates. However, a blend of $N^{*}$ resonances at ∼1400 GeV can contribute as much as 20% to the neutrino flux. It is further found that $\gamma$–He interactions produce approximately 10% of astrophysical pions, as compared with $\gamma p$ interactions. Full article

The BESIII Collaboration has collected large data samples from $e^{+}{e}^{-}$ collisions at center-of-mass energies ranging from 1.84 to 4.95 GeV, which include the world’s largest charmonium sample, consisting of 10 billion $J/\psi$ and 3 billion $\psi \left(3686\right)$ events. These high-statistics datasets enable BESIII to carry out a wide range of studies in hyperon physics. In this article, we review the major achievements of the BESIII Collaboration in this field, which can be broadly categorized into four areas: hyperon polarization and $CP$ violation, rare hyperon decays, hyperon pair production, and hyperon–nucleon interactions. Full article

Identified-hadron production (p, ${\pi }^{\pm }$, $K^{\pm }$) in ${\pi }^{+}+\mathrm{Be}$ at $p_{\mathrm{lab}}=60\mathrm{GeV}/c$ ($\sqrt{s}\simeq 10.6\mathrm{GeV}$) is investigated using Pythia 8.315 (Monash tune) with the Angantyr extension. Differential multiplicities $d^{2}n/\left(dpd\theta \right)$ are confronted with NA61/SHINE measurements across standard $\theta$ bins. Within the fluctuating-radii Double-Strikman (DS) scheme, two unsuppressed opacity mappings are compared to quantify systematics. In addition, a minimal extension is introduced: a flat, post-classification, channel-wise acceptance applied after ND/SD/DD/EL tagging. It acts on primary and secondary $\pi N$ pairs, keeps hadronization fixed (Lund string), and leaves the internal event generation of each admitted subcollision unchanged. Opacity-mapping variations alone induce only percent-level differences and do not resolve the soft/forward tensions. By contrast, the flat acceptance—interpretable as a reduced effective ND weight—improves agreement across species and angles. It hardens the forward ${\pi }^{+}$ spectra and lowers large-$\theta$ yields, produces milder charge-asymmetric changes for ${\pi }^{-}$ consistent with the weaker leading feed, suppresses proton yields at all angles (with a residual $\sim 30\%$ forward high-p deficit), and improves $K^{\pm }$, with a stronger effect for $K^{+}$ than $K^{-}$. These results show that a geometry-blind reweighting of the subcollision mixture suffices to capture the main NA61/SHINE trends for ${\pi }^{+}+\mathrm{Be}$ at SPS energies without modifying hadronization. The approach provides a controlled baseline for subsequent, channel-balanced refinements and broader ${\pi }^{+}A$ tuning. Full article