Search Results (1,237)

Background: Guillain–Barré syndrome (GBS) is an autoimmune peripheral neuropathy that can lead to paralysis and respiratory failure. In addition to infections, several drugs have been suggested as potential triggers of GBS. This study investigated drug-associated GBS using a spontaneous adverse event reporting database through disproportionality analysis for signal detection and time-to-onset analysis. Methods: The Japanese Adverse Drug Event Report (JADER) database was analyzed to assess more than 4000 drugs for potential associations with GBS. Signal detection was performed using reporting odds ratios, Fisher’s exact test, and total report counts. For vaccines and immune checkpoint inhibitors, time-to-onset patterns were further evaluated using Weibull distribution analysis. Results: Disproportionality signals suggesting potential associations with GBS were identified for 45 drugs, including vaccines, immune checkpoint inhibitors, tumor necrosis factor-α inhibitors, other anticancer drugs, antifungal agents, and interferons. Reports following vaccination were most frequently observed within 1–3 weeks after administration of coronavirus disease 2019 (COVID-19), influenza, and pneumococcal vaccines, and within 1–3 months after human papillomavirus 2-valent vaccination, with a gradual decrease thereafter. Reports following immune checkpoint inhibitor use were most frequently observed 1–3 months after nivolumab, ipilimumab, and pembrolizumab administration, whereas atezolizumab showed a peak in reporting within 1–3 weeks. In contrast to vaccine-related reports, no clear temporal trend in reporting was observed. Conclusions: Drugs that modulate immune function, including vaccines and immune checkpoint inhibitors, may be associated with reported GBS events. Vaccine-related reports showed an early concentration in time to onset, whereas immune checkpoint inhibitor-related reports did not demonstrate a clear temporal pattern. These findings should be interpreted as hypothesis-generating and warrant further investigation. Full article

Corrosion of steel pipe specimens in marine environments plays a critical role in the durability and service-life design of coastal and offshore structures. In Vietnam, the scarcity of long-term field corrosion data necessitates the application of accelerated testing and statistical modeling to characterize corrosion degradation. In this study, a two-parameter Weibull model is employed to describe the time-dependent corrosion mass loss of steel pipe specimens under simulated Vietnamese marine conditions. Accelerated corrosion tests are conducted using an impressed current technique in artificial seawater, and equivalent exposure durations ranging from 4.5 to 100 years are determined based on Faraday’s law. This conversion is based on the assumption of uniform corrosion and constant electrochemical conditions, which may not fully represent real marine environments. The Weibull parameters are calibrated using early-stage corrosion data, yielding a shape parameter k = 1.226 and a scale parameter η = 70.761 years. Comparison with experimental results indicates that the model captures the monotonic increase in cumulative corrosion mass loss, although it overestimates the measurements at intermediate exposure durations. The validation results show prediction errors of MAE = 13.06% and RMSE = 14.13%, while sensitivity analysis reveals that long-term predictions are more sensitive to the shape parameter than to the scale parameter. This study also discusses the limitations of using accelerated corrosion testing and Faraday’s law for scaling to long-term predictions, particularly regarding differences in corrosion product morphology and the impact of real-world environmental variability. The calibrated Weibull model provides a statistical approximation for durability assessment of steel pipe structures under Vietnamese marine conditions, particularly in cases where long-term field corrosion data are unavailable. Full article

Control charts for monitoring time between events (T) and amplitude (X) have been developed in recent years. Many TBEA charts depend on limited models such as exponential, normal, and gamma distributions and mainly rely on the ratio statistic (${\displaystyle \frac{X}{T}}$). This representation ignores the symmetric relationship between event occurrence and event magnitude. This paper proposes Shewhart-type TBEA charts constructed from three statistics $\left(Z_1\right)$, $\left(Z_2\right)$, and $\left(Z_3\right)$ based on $\left(X\right)$ and $\left(T\right)$. The approach models symmetry between frequency and amplitude using generalized Weibull and generalized log-logistic distributions. The statistics maintain proportional invariance when both variables shift together, which enables balanced monitoring of the process. Several scenarios are examined for detecting upward shifts. Performance is assessed using numerical measures of detection efficiency and average run length. The results show improved detection compared with classical ratio-based TBEA charts. A real data example from a French forest fire database illustrates the ability of the proposed charts to detect simultaneous changes in occurrence rate and burn intensity. Full article

Uncertainties in generation and dynamic load behavior provide new problems for radial distribution systems (RDS) caused by the growing integration of renewable distributed generators (RDGs), including solar photovoltaic (PV) systems and wind turbines (WTs), as well as electric vehicle charging stations (EVCS). This article offers a thorough techno-economic evaluation of how to best distribute RDG resources (solar PV, wind, and EVCS) inside a 28-bus distribution test system in India, taking into account generation volatility due to the seasons. Optimization of installation and operating costs, enhancing voltage stability, and decreasing active power loss are done all at once using a new Catch Fish Optimization Algorithm (CFOA). Integrating beta and Weibull distributions, respectively, into the probabilistic modeling of solar irradiance and wind speed allows for economic analysis to adhere to recognized approaches from contemporary multi-objective optimization frameworks. The simulation findings confirm that the proposed CFOA-based placement method improves economic efficiency, decreases energy loss, and increases system performance. Full article

Recently, two novel extensions of the Weibull distribution have been introduced through Manly’s exponential transformation, offering a flexible mechanism for modeling skewness, tail behavior, and complex hazard rate structures. In this study, we develop a comprehensive theoretical and inferential framework for one of these models, referred to as the Baker–T1 distribution, to establish it as a mature and practically viable lifetime model for reliability and survival analysis. While the Baker–T1 model exhibits remarkable flexibility in capturing skewness, tail behavior, and complex hazard rate shapes, its statistical properties and practical performance have not yet been systematically investigated. To bridge this gap, we derive a wide range of fundamental distributional characteristics, including reliability measures, hazard and reversed-hazard functions, quantiles, moments, skewness, kurtosis, dispersion indices, and order statistics, establishing the model’s analytical tractability and structural richness. An extensive inferential framework is introduced by implementing eight classical estimation techniques, and their finite-sample behavior is rigorously examined through a large-scale Monte Carlo simulation study under diverse parameter configurations. The practical relevance of the Baker–T1 model is further demonstrated using two genuine datasets from biomedical and engineering domains, where it consistently outperforms thirteen competing lifetime distributions according to likelihood-based and information-theoretic criteria. Full article

Accurately characterizing the statistical fluctuations of vehicle radar cross sections (RCSs) across polarization states and azimuthal sectors is essential for evaluating detection performance, conducting probabilistic simulations, and analyzing target features in millimeter-wave radar systems. Existing one-dimensional RCS statistical models, including Weibull, Chi-square, Lognormal, Rice, and Gaussian distributions, are often limited by their restricted functional expressiveness, making it difficult to simultaneously capture skewness, tail thickness, and azimuthal dependence under narrow angular-domain conditions. In addition, purely nonparametric approaches tend to produce spurious modes under finite-sample conditions and lack interpretable structural priors. To address these limitations, this paper proposes a Unimodal RCS Semiparametric Density Estimator (URCS-SDE) tailored for ground-vehicle targets. The proposed approach adopts kernel density estimation (KDE) as a data-driven baseline representation and incorporates physically plausible structural constraints through unimodal shape projection. Then a beta-type tail template is further introduced in the normalized amplitude domain to regulate boundary decay behavior. Finally, weighted least-squares calibration is performed on the histogram grid of the empirical probability density function (PDF), achieving a balanced trade-off between fitting accuracy and stability in both the peak and tail regions. Using multi-azimuth RCS measurements of two representative ground vehicles, the URCS-SDE is systematically compared with five classical parametric distributions and a representative regularized mixture density network (MDN) baseline. Performance is evaluated under both full-azimuth and directional-window conditions using the sum of squared errors (SSE), root mean squared error (RMSE), coefficient of determination (R-square) and held-out negative log-likelihood (NLL). The results show that the URCS-SDE consistently provides the most accurate and stable density estimates, especially in narrow angular windows. In addition, a threshold-based detection-support example derived from the fitted PDFs demonstrates that the advantage of the URCS-SDE transfers from density reconstruction to a directly engineering-relevant downstream quantity. Full article

Camellia japonica is a widely cultivated woody ornamental plant. However, current studies mostly focus on the onset of flowering, neglecting the overall flowering time distribution patterns of the blooming process. In this study, we used uniform 5-year-old potted cuttings of C. japonica ‘Jinjiang Mudan’ to evaluate six candidate distribution models, including normal, log-normal, skew-normal, gamma, Weibull, and exponential, to model flowering time distribution. These candidates were compared to obtain an optimal distribution model using three-fold cross-validation, six evaluation indicators, and three goodness-of-fit tests in the control. The optimal distribution model was used to compare and analyze the different effects of the control, shading, and exogenous hormone treatments. The results showed that the skew-normal distribution model emerged as the most suitable distribution model among the six candidates and captured the flowering time distribution characteristics effectively in all treatments. Shading treatments were found to delay and extend the flowering period, with moderate treatments (50% and 70% shading) demonstrating better performance, extending the flowering period by approximately 40%. In terms of exogenous hormone treatments, BG (a mixture of the 6-BA and GA₃) concentrations could prolong and delay the flowering period. Lower concentrations (100 and 250 mg L⁻¹) of 6-BA and GA₃ were effective in extending the flowering period, with BA250 exhibiting the most pronounced effect, delaying flowering onset by approximately 12% and extending the flowering period by approximately 17%. Considering that this study is based on single-location and single-season trials, these findings provide a valuable methodological resource for quantifying and predicting flowering time distribution in C. japonica, other ornamentals, and crops. Full article

We propose a sequential design method aiming at the estimation of an extreme quantile based on a sample of binary data corresponding to peaks over a given threshold. This study is motivated by an industrial challenge in material reliability and consists of estimating a failure quantile from trials whose outcomes are reduced to indicators of whether the specimen has failed at the tested stress levels. The proposed approach relies on a splitting strategy that decomposes the target extreme probability into a product of higher-order conditional probabilities, enabling a progressive exploration of the tail of the distribution through sampling under truncated laws. We consider GEV and Weibull models for the underlying distribution, and the sequential estimation of their parameters is carried out using an enhanced maximum likelihood procedure specifically adapted to binary data, addressing the substantial uncertainty inherent to such limited information. Full article

Gaseous emissions from coal combustion during electricity generation continue to be a challenge in South Africa. To meet the regulatory limits, it is crucial to understand the statistical distribution of such emissions from the power generating plants. The current paper characterises the nitrogen dioxide (NO₂) emissions from Eskom’s Majuba coal-fired power station by making use of the quantile–quantile (QQ) plots and derivative plots of three statistical parent distributions, namely, the Weibull, Lognormal, and Pareto distributions. These distributions are fitted and compared according to their tail heaviness as they cater for data that may have tails lighter or heavier than that of the Exponential distribution. Of the three distributions evaluated here, the Lognormal gave the best fit for the full body of the data according to the QQ and derivative plots, and the goodness-of-fit tools (bootstrap Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Akaike Information Criterion (AIC), Schwarz’s Bayesian Information Criterion (BIC), and the BIC-corrected Vuong test for non-nested distributions). The Lognormal distribution also gave the best fit for the overall upper tail, while at the very top six largest NO₂ emission observations in the upper tail, a Pareto-type tail was observed. The practical implication of a heavy tail like the Pareto is that it models more frequent larger sized NO₂ emissions compared to lighter tails like the Weibull and Lognormal tails. The methods used in this study give a framework on how emissions of NO₂ from a coal-fired power station can be modelled using statistical parent distributions whilst also taking into account the distribution of the data in the tails which is mostly ignored when fitting statistical parent distributions. Understanding the distribution of the upper tail is very important since higher and rare emissions are of the most concern and are dangerous to human health and the environment. Full article

Time headway is a key parameter for describing car-following behavior and microscopic traffic flow characteristics, and it is important for traffic safety analysis, road design, and optimizing intelligent-driving strategies. Existing research offers limited insight into the heterogeneity of time headway under different vehicle types and lane conditions. It is particularly important to investigate how time headway distributions differ across lane–vehicle-type combinations on highways, as these differences can affect safety evaluation and operational performance. This study is based on drone-captured vehicle trajectories from the publicly available HighD dataset. We select 378,751 vehicle–frame trajectory records; these records are used to construct valid follower–leader pairs and derive time headway (THW) samples for distribution fitting. Eight subsets are formed by combining two lane positions (inner vs. outer) and four follower–leader vehicle-type pairs (car–car, car–truck, truck–car, truck–truck). Six candidate distributions (Lognormal, Log-logistic, Burr, Weibull, Gamma, and Logistic) are fitted using maximum likelihood estimation, and their fit is evaluated using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square tests, which are fused via an entropy-weighted composite score for model ranking. Results show pronounced heterogeneity across lane–vehicle-type subsets: Inner-lane samples exhibit smaller and more concentrated time gaps, whereas outer-lane samples show larger mean gaps, stronger dispersion, and heavier upper tails. Overall, Lognormal(3P) is selected as the top-ranked model in 5 of 8 subsets (62.5%), while Burr(4P) (car–truck, outer lane), Gamma(3P) (truck–car, outer lane), and Weibull(3P) (truck–truck, inner lane) are optimal in the remaining subsets. These findings indicate that lane position and vehicle-type pairing materially affect THW distributional characteristics, providing quantitative guidance for lane- and vehicle-aware traffic modeling, safety-oriented assessment, and intelligent-driving strategy design. Full article

Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

The life insurance sector plays a critical role in financial system stability but is inherently exposed to extreme market fluctuations due to long-term liabilities and asset–liability mismatches. This study investigates extreme losses in the growth rates of the JSE Life Insurance Price Index (LIPI) using the Generalised Extreme Value Distribution (GEVD) and the Generalised Pareto Distribution (GPD) under the Extreme Value Theory (EVT) framework. Monthly data from January 2000 to October 2023 were transformed into a loss series, and extreme events were captured using quarterly block maxima and a POT threshold at the 95th percentile. Model parameters were estimated through Maximum Likelihood Estimation, and downside risk was assessed using return levels, Value-at-Risk (VaR), and Tail Value-at-Risk (tVaR). The GEVD model produced a negative shape parameter, consistent with a bounded Weibull-type tail, while the GPD indicated a heavy-tailed distribution. Return level estimates show escalating loss magnitudes and widening uncertainty over longer horizons, reflecting the challenges of projecting rare events. Kupiec backtesting confirms the adequacy and reliability of the GEVD-based VaR across all confidence levels, whereas the GPD underestimates risk at lower thresholds. These findings indicate significant tail risk within the South African life insurance equity segment and underscore the importance of EVT-based risk measures for capital planning and regulatory oversight. The study contributes to financial risk modelling in the life insurance sector and offers practical insights for strengthening solvency assessment and enterprise risk management frameworks. Full article

The main focus of this paper is to introduce a new probability model. Specifically, this paper presents a modified form of the Weibull distribution and investigates its various statistical properties, such as moments, moment-generating functions, reliability functions, quantile functions, and inequality measures such as Bonferroni and Lorenz curves. It also investigates the mean absolute deviation and entropy. Distributions of order statistics, reversed order statistics, and upper record values are also obtained. Additionally, univariate and bivariate moment structures are considered. The model parameters are estimated via the maximum likelihood method under simple random sampling and ranked set sampling, allowing an empirical evaluation of efficiency and reliability. Graphical representations exhibit the flexibility of the model, capturing various shapes in the probability density and hazard rate functions. To measure the practical quality of the model, actuarial metrics are used. A comparative analysis based on insurance, biomedical, and reliability datasets demonstrates the empirically improved performance and stability of the proposed new model for these specific datasets. Full article

This study collected 155 sets of test data for granite residual soils from the Fujian–Guangdong region and applied the chi-square test to analyze the distributions of eight common physical and mechanical parameters. Drained triaxial tests were then simulated using the Unified Hardening (UH) model, and a Sobol global sensitivity analysis of model parameters was conducted based on the distributions of soil properties. The results show that natural density and cohesion approximately follow Weibull distributions; void ratio, liquid limit and plastic limit follow lognormal distributions; water content and internal friction angle follow normal distributions; and plasticity index follows a Gumbel distribution. The Sobol analysis indicates that the critical state deviatoric stress mainly depends on the critical state stress ratio (M), the critical state volumetric strain is jointly controlled by M and the slope of the normal compression line (λ). The overall evolution of deviatoric stress mainly depends on M, and the overall evolution of volumetric strain mainly depends on λ, whereas Poisson’s ratio (ν) has little influence on the soil stress–strain response. These findings provide references for parameter selection and numerical simulation of granite residual soils in the Fujian–Guangdong region. Full article