Search Results (535)

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

The estimation of runout distances for long return period avalanches is vital in zoning schemes for mountainous countries. There are two broad methods to estimate snow avalanche runout distance. One involves the use of a physical model to calculate speeds along the incline, with runout distance determined when the speed drops to zero. The second method, which is used here, is based on empirical or statistical models from databases of extreme runout for a given mountain range or area. The second method has been used for more than 40 years with diverse datasets collected from North America and Europe. The primary reason for choosing the method used here is that it is independent of physical models such as avalanche dynamics, which allows comparisons between methods. In this paper, data from diverse datasets are analyzed to explain the relation between them to give an overall view of the meaning of the data. Runout is formulated from nine different datasets and 738 values of extreme runout, mostly with average return periods of about 100 years. Each dataset was initially fit to 65 probability density functions (pdf) using five goodness-of-fit tests. Detailed discussion and analysis are presented for a set of extreme value distributions (Gumbel, Frechet, Weibull). Two distributions had exemplary results in terms of goodness of fit: the generalized logistic (GLO) and the generalized extreme value (GEV) distributions. Considerations included both the goodness-of-fit and the heaviness of the tail, of which the latter is important in engineering decisions. The results showed that, generally, the GLO has a heavier tail. Our paper is the first to compare median extreme runout distances, the first to compare exceedance probability of extreme runout, and the first to analyze many probability distributions for a diverse set of datasets rigorously using five goodness-of-fit tests. Previous papers contained analysis mostly for the Gumbel distribution using only one goodness-of-fit test. Given that climate change is in effect, consideration of stationarity of the distributions is considered. Based on studies of climate change and avalanches, thus far, it has been suggested that stationarity should be a reasonable assumption for the extreme avalanche data considered. Full article

This paper explores statistical techniques for estimating unknown lifetime parameters using data from a progressive first-failure censoring scheme. The failure times are modeled with a new Weibull–Pareto distribution. Maximum likelihood estimators are derived for the model parameters, as well as for the survival and hazard rate functions, although these estimators do not have explicit closed-form solutions. The Newton–Raphson algorithm is employed for the numerical computation of these estimates. Confidence intervals for the parameters are approximated based on the asymptotic normality of the maximum likelihood estimators. The Fisher information matrix is calculated using the missing information principle, and the delta technique is applied to approximate confidence intervals for the survival and hazard rate functions. Bayesian estimators are developed under squared error, linear exponential, and general entropy loss functions, assuming independent gamma priors. Markov chain Monte Carlo sampling is used to obtain Bayesian point estimates and the highest posterior density credible intervals for the parameters and reliability measures. Finally, the proposed methods are demonstrated through the analysis of a real dataset. Full article

This study evaluates leadership uniformity—the degree to which the proposer role is evenly distributed among validator nodes over time—in Quorum-based Byzantine Fault Tolerance (QBFT), a Byzantine Fault-Tolerant (BFT) consensus algorithm used in permissioned blockchain networks. By introducing simulated follower timeouts derived from uniform, normal, lognormal, and Weibull distributions, it models a range of network conditions and latency patterns across nodes. This approach integrates Raft-inspired timeout mechanisms into the QBFT framework, enabling a more detailed analysis of leader selection under different network conditions. Three leader selection strategies are tested: Direct selection of the node with the shortest timeout, and two quorum-based approaches selecting from the top 20% and 30% of nodes with the shortest timeouts. Simulations were conducted over 200 rounds in a 10-node network. Results show that leader selection was most equitable under the Weibull distribution with shape $k=0.5$, which captures delay behavior observed in real-world networks. In contrast, the uniform distribution did not consistently yield the most balanced outcomes. The findings also highlight the effectiveness of quorum-based selection: While choosing the node with the lowest timeout ensures responsiveness in each round, it does not guarantee uniform leadership over time. In low-variability distributions, certain nodes may be repeatedly selected by chance, as similar timeout values increase the likelihood of the same nodes appearing among the fastest. Incorporating controlled randomness through quorum-based voting improves rotation consistency and promotes fairer leader distribution, especially under heavy-tailed latency conditions. However, expanding the candidate pool beyond 30% (e.g., to 40% or 50%) introduced vote fragmentation, which complicated quorum formation in small networks and led to consensus failure. Overall, the study demonstrates the potential of timeout-aware, quorum-based leader selection as a more adaptive and equitable alternative to round-robin approaches, and provides a foundation for developing more sophisticated QBFT variants tailored to latency-sensitive networks. Full article

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R² and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions. Full article

This study provides a comprehensive analysis of the fatigue cracking behavior of super martensitic stainless steel in air and water environments, highlighting the critical influence of environmental factors on its mechanical properties. By examining the distribution of fatigue test data, the Weibull three-parameter model was identified as the most accurate descriptor of fatigue life data in both environments. Key findings reveal that, in air, cracks predominantly propagate along the densest crystallographic planes, whereas, in water, corrosive media significantly accelerate crack initiation and propagation, reducing fatigue resistance, creating more tortuous crack paths, and inducing microvoids and secondary cracks at the crack tip. These corrosive effects adversely alter the material’s microstructure, profoundly impacting fatigue life and crack propagation rates. The insights gained from this research are crucial for understanding the performance of super martensitic stainless steel in aqueous environments, offering a reliable basis for its engineering applications and contributing to the development of more effective design and maintenance strategies. Full article

Background/Objectives: The combination drug sulfamethoxazole/trimethoprim (ST) is a broad-spectrum antibiotic used against various infections; however, it is associated with several serious adverse events. The ST package inserts contain warnings about these adverse events. However, warnings vary internationally, and specific measures to address ST-related adverse events are unclear. Therefore, we aimed to comprehensively evaluate ST-related adverse events using the Japanese Adverse Drug Event Report (JADER) database and analyze the onset time for each event. Methods: Adverse events due to ST were analyzed using the JADER database between April 2004 and June 2023. The reported odds ratio and 95% confidence interval (95% confidence interval [CI]) were calculated, with a signal detected if the 95% CI lower limit exceeded 1. The Weibull distribution was used to characterize the onset time of adverse events with detected signals. Results: The total number of cases in the JADER database during the study period was 862,952, and the number of adverse events involving ST as a suspected drug was 4203. Adverse events associated with ST include hyperkalemia, syndrome of inappropriate antidiuretic hormone secretion, hematopoietic cytopenia, acute renal failure, hypoglycemia, disseminated intravascular coagulation syndrome, hepatic disorder, and the Stevens–Johnson syndrome/toxic epidermal necrolysis. Conclusions: Weibull analysis indicated an early failure-type onset time for all adverse events, suggesting the need for intensive adverse event monitoring of ST, especially in the first month of use. These findings may support revising drug package inserts in Japan to better reflect the identified risks. Full article

Outlier detection plays a key role in data analysis by improving data quality, uncovering data entry errors, and spotting unusual patterns, such as fraudulent activities. Choosing the right detection method is essential, as some approaches may be too complex or ineffective depending on the data distribution. In this study, we explore a simple yet powerful approach using the range distribution to identify outliers in univariate data. We compare the effectiveness of two range statistics: we normalize the range by the standard deviation ($\sigma$) and the interquartile range (IQR) across different types of distributions, including normal, logistic, Laplace, and Weibull distributions, with varying sample sizes (n) and error rates ($\alpha$). An evaluation of the range behavior across multiple distributions allows for the determination of threshold values for identifying potential outliers. Through extensive experimental work, the accuracy of both statistics in detecting outliers under various contamination strategies, sample sizes, and error rates ($\alpha =0.1,0.05,0.01$) is investigated. The results demonstrate the flexibility of the proposed statistic, as it adapts well to different underlying distributions and maintains robust detection performance under a variety of conditions. Our findings underscore the value of an adaptive method for reliable anomaly detection in diverse data environments. Full article

Background/Objectives: Mesoporous silica materials, particularly KIT-6, offer promising features, such as large surface area, tunable pore structures, and biocompatibility, making them ideal candidates for advanced drug delivery systems. The aims of this study were to develop and evaluate an innovative modified-release platform for metformin hydrochloride (MTF), using KIT-6 mesoporous silica as a matrix, to enhance oral antidiabetic therapy. Methods: KIT-6 was synthesized using an ultrasound-assisted sol-gel method and subsequently loaded with MTF via adsorption from alkaline aqueous solutions at two concentrations (1 and 3 mg/mL). The structural and morphological characteristics of the matrices—before and after drug loading—were assessed using SEM-EDX, TEM, and nitrogen adsorption–desorption isotherms (the BET method). In vitro drug release profiles were recorded in simulated gastric and intestinal fluids over 12 h. Kinetic modeling was performed using seven classical models, and a multifractal theoretical framework was used to further interpret the complex release behavior. Results: The loading efficiency increased with increasing drug concentration but nonlinearly, reaching 56.43 mg/g for 1 mg/mL and 131.69 mg/g for 3 mg/mL. BET analysis confirmed significant reductions in the surface area and pore volume upon MTF incorporation. In vitro dissolution showed a biphasic release: a fast initial phase in an acidic medium followed by sustained release at a neutral pH. The Korsmeyer–Peppas and Weibull models best described the release profiles, indicating a predominantly diffusion-controlled mechanism. The multifractal model supported the experimental findings, capturing nonlinear dynamics, memory effects, and soliton-like transport behavior across resolution scales. Conclusions: The study confirms the potential of KIT-6 as a reliable and efficient carrier for the modified oral delivery of metformin. The combination of experimental and multifractal modeling provides a deeper understanding of drug release mechanisms in mesoporous systems and offers a predictive tool for future drug delivery design. This integrated approach can be extended to other active pharmaceutical ingredients with complex release requirements. Full article

This study focuses on estimating the unknown parameters and the reliability function of the inverted-Weibull distribution, using an improved adaptive progressive Type-II censoring scheme under a competing risks model. Both classical and Bayesian estimation approaches are explored to offer a thorough analysis. Under the classical approach, maximum likelihood estimators are obtained for the unknown parameters and the reliability function. Approximate confidence intervals are also constructed to assess the uncertainty in the estimates. From a Bayesian standpoint, symmetric Bayes estimates and highest posterior density credible intervals are computed using Markov Chain Monte Carlo sampling, assuming a symmetric squared error loss function. An extensive simulation study is carried out to assess how well the proposed methods perform under different experimental conditions, showing promising accuracy. To demonstrate the practical use of these methods, a real dataset is analyzed, consisting of the survival times of male mice aged 35 to 42 days after being exposed to 300 roentgens of X-ray radiation. The analysis demonstrated that the inverted Weibull distribution is well-suited for modeling the given dataset. Furthermore, the Bayesian estimation method, considering both point estimates and interval estimates, was found to be more effective than the classical approach in estimating the model parameters as well as the reliability function. Full article

Flooding is a recurrent natural hazard in Romania, causing significant socio-economic impacts. Historical data highlights the severity of floods, particularly the unprecedented flood of 1926. Between 1960 and 2010, Romania experienced over 400 major floods, which significantly impacted its infrastructure and population. Particularly, the floods in 2005 and 2006 affected over 1.5 million people, resulting in 93 deaths and causing damages exceeding EUR 2 billion. In compliance with the Floods Directive, EU member states must assess and map flood hazards and risks. This study aims to develop a frequency analysis to determine discharges as a predictive indicator for different hazard levels: frequent events (10-year return period), medium probability events (100-year return period), and extreme events. The Casimcea catchment in central Dobrogea, drained by the Casimcea River into Lake Tasaul, serves as the study area. The annual maximum discharge data analysis, conducted through frequency analysis and the ELECTRE method, indicates that EV3-Min-Weibull, L-moments, and GEV-Min (L-moments) are the most effective probability density functions (PDFs). To conclude, although a single PDF model cannot be determined for the Casimcea River and its tributaries, it contributes to predictive modeling efforts. Full article

Green roofs are increasingly recognized as effective Nature-Based Solutions (NBS) for urban stormwater management, contributing to sustainable and climate-resilient cities. The Soil Conservation Service Curve Number (SCS-CN) model is commonly used to simulate their hydrological performance due to its simplicity and low data requirements. However, the standard assumption of a fixed initial abstraction ratio (Ia/S = 0.2), long debated in hydrology, has been largely overlooked in green roof applications. This study investigates the variability of Ia/S and its impact on runoff simulation accuracy for a green roof under a humid subtropical climate. Event-based analysis across multiple storms revealed Ia/S values ranging from 0.01 to 0.62, with a calibrated optimal value of 0.17. This variability is primarily driven by the physical and biological characteristics of the green roof rather than short-term rainfall conditions. Using the fixed ratio introduced consistent biases in runoff estimation, while intermediate ratios (0.17–0.22) provided higher accuracy, with the optimal ratio yielding a median Curve Number (CN) of 89 and high model performance (NSE = 0.95). Additionally, CN values followed a positively skewed Weibull distribution, highlighting the value of probabilistic modeling. Though limited to one green roof design, the findings underscore the importance of site-specific parameter calibration to improve predictive reliability. By enhancing model accuracy, this research supports better design, evaluation, and management of green roofs, reinforcing their contribution to integrated urban water systems and global sustainability goals. Full article

This study investigates the effect of boron carbide (B₄C) ceramic reinforcement on the microstructural, mechanical, electrical, and electrochemical properties of CoCrMo, Ti, and 17-4 PH alloys produced via powder metallurgy for potential biomedical applications. A systematic experimental design was employed, incorporating varying B₄C contents into each matrix through mechanical alloying, cold pressing, and vacuum sintering. The microstructural integrity and dispersion of B₄C were examined using scanning electron microscopy. The performance of the materials was evaluated using several methods, including Vickers hardness, pin-on-disk wear testing, ultrasonic elastic modulus measurements, electrical conductivity, and electrochemical assessments (potentiodynamic polarization and EIS). This study’s findings demonstrated that B₄C significantly enhanced the hardness and wear resistance of all alloys, especially Ti- and CoCrMo-based systems. However, an inverse correlation was observed between B₄C content and corrosion resistance, especially in 17-4 PH matrices. Ti-5B₄C was identified as the most balanced composition, exhibiting high wear resistance, low corrosion rate and elastic modulus values approaching those of human bone. Weibull analysis validated the consistency and reliability of key performance metrics. The results show that adding B₄C can change the properties of biomedical alloys, offering engineering advantages for B₄C-reinforced biomedical implants. Ti-B₄C composites exhibit considerable potential for application in advanced implant technologies. Full article

This paper discusses the Bayesian estimation for the unknown parameters, reliability and hazard rate functions of the doubly truncated Zubair-Weibull distribution. Informative priors (gamma distribution) for the parameters are used to obtain the posterior distributions. Under the squared-error and linear–exponential loss functions, the Bayes estimators are derived. Credible intervals for the parameters, reliability and hazard rate functions are obtained. Bayesian prediction (point and interval) for the future observation is considered under the two-sample prediction scheme. A simulation study is performed using the Markov Chain Monte Carlo algorithm of simulation for different sample sizes to assess the performance of the estimators. Two real datasets are applied to show the flexibility and applicability of the distribution. Full article

Bayesian methods have attracted increasing interest in the design and analysis of clinical trials. Many of these clinical trials investigate time-to-event endpoints. The Weibull distribution is often used in survival and reliability analysis to model time-to-event data. We propose a process to elicit information about the parameters of the Weibull distribution for pharmaceutical applications. Our method is based on an expert’s answers to questions about the median and upper quartile of the distribution. Using the elicited information, a joint prior is constructed for the median and upper quartile of the Weibull distribution, which induces a joint prior distribution on the shape and rate parameters of the Weibull. To illustrate, we apply our elicitation methodology to a pediatric clinical trial, where information is elicited from a subject-matter expert for the control arm. Full article