This study addresses a significant gap in evaluations of the safety of antibacterial drugs in infants aged 0-1year on the basis of a comprehensive pharmacovigilance analysis. We use data spanning the period from 2005Q1 to 2025Q1, which we obtain from the FDA Adverse Event Reporting System (FAERS) to retrospectively assess adverse events (AEs) associated with three commonly used antibacterials-vancomycin, cefotaxime, and gentamicin-in this vulnerable population. A total of 698 cases of AE were included in the study. Disproportionality analyses (which rely on the reporting odds ratio (ROR), proportional odds ratio (PRR), Bayesian confidence propagation neural network (BCPN), and multi-item gamma Poisson shrinker (MGPS)) revealed significant AE signals with multiple testing correction using a 5% false discovery rate: vancomycin and gentamicin were predominantly associated with acute kidney injury (n = 50, ROR = 16.18, 95% CI 12.13-21.59, P = 3.16e - 41; n = 12, ROR = 21.18, 95% CI 11.82-37.95, P = 1.95e - 12), whereas cefotaxime was linked to increased alanine aminotransferase (n = 4, ROR = 7.72, 95% CI 2.87-20.75, P = 0.002) and aspartate aminotransferase levels (n = 4, ROR = 7.51, 95% CI 2.79-20.19, P = 0.002), thus indicating liver injury. Gentamicin was also associated with neurosensory deafness (n = 6, ROR = 146.47, 95% CI 63.31-338.83, P = 8.85e - 12). A Weibull distribution analysis of onset timing showed that the risk of AEs for all three antibacterials remained consistent over time. These findings underscore the increased vulnerability of infants to drug toxicity. These results emphasize the critical need for cautious, individualized risk-benefit assessments in the context of prescribing antibacterials to infant patients.

The agricultural sector confronts escalating challenges, including population growth, climate change, and constrained land resources. Addressing these issues requires agricultural methods to evolve into more intelligent, sustainable, and efficient practices in order to satisfy the expanding global demand. In this context, the smart greenhouse plays a crucial role in contemporary smart agriculture by amalgamating diverse technologies and equipment to establish an optimal environment for plant cultivation. This paper aims to create an enhanced algorithm for ventilation control utilizing artificial intelligence technology. The goal is to achieve intelligent ventilation and air exchange in greenhouses while ensuring that optimal air conditions for crop growth are maintained consistently. Employing data from the Shouguang Vegetable High-Tech Demonstration Park, we collect and analyze historical and real-time data within a glass greenhouse. We establish the foundational algorithm for autonomous ventilation control using an adaptive genetic algorithm-back propagation neural network. A hybrid fitness scaling approach, combining linear and nonlinear fitness scaling, is proposed and implemented. In the pursuit of a well-fitted model, three models i.e., multiple regression (MR), back propagation neural network (BPNN), and genetic algorithm - back propagation neural network (GA-BPNN) are explored in experiments to fit greenhouse data. These models have been validated through extensive simulation experiments, and the results revealed that our method outperforms the investigated techniques in terms of errors. This paper concludes that effective ventilation control algorithms enable precise regulation of greenhouse environmental factors, including temperature, humidity, and CO2, thereby optimizing crop yield and quality.

This study offers a new probability distribution to improve the modeling of lifetime data. The main statistical properties of the distribution, including its quantile function, moments, and moment generating function, are derived. The model parameters are estimated using the method of maximum likelihood (MLE), and a simulation study is conducted to evaluate the performance and accuracy of the MLEs under different sample sizes and parameter settings. To demonstrate its practical usefulness, the proposed distribution is applied to three real datasets and compared with several existing competing distributions. The goodness-of-fit tests show that the proposed distribution fits the data better than the competing models.

This study investigates the quasi-static crashworthiness of circular composite tubes fabricated from glass (G), aramid (A), and hybrid glass-aramid (G/A) fibers embedded within an epoxy resin matrix. The tubes were fabricated using the hand lay-up wet-wrapping technique. Under uniaxial quasi-static compression, the crash load and corresponding energy absorption were recorded as functions of axial displacement, while deformation histories were monitored to characterize failure mechanisms. The crashworthiness evaluation focused on key indicators, including the initial peak load ( P ip ), total absorbed energy (AE), mean crash load ( P avg ), crash force efficiency (CFE), and specific energy absorption (SEA). To quantify variability and failure probability, a two-parameter Weibull distribution was fitted using the least-squares method. The results demonstrated that the hybrid configuration 4A/4G exhibited the highest P ip = 9.66 kN, which is approximately 256% higher than that of the pure aramid tube (8A, P ip = 2.71 kN). In contrast, the pure glass tube (8G) achieved the highest AE = 404.93 J, corresponding to an increase of 220% compared to the lowest-performing hybrid (A/2G/2A/2G/A, AE = 126.38 J). Similarly, the 8G specimen recorded the highest P avg = 5.47 kN, 238% greater than that of the lowest (1.62 kN for A/2G/2A/2G/A), and exhibited the highest CFE = 93.71% and SEA = 9.62 J/g, which are approximately 241% and 192% higher than those of the same hybrid, respectively. The 4A/4G hybrid displayed the lowest CFE = 27.47. The failure probability plots provide a practical tool for predicting performance variations and guiding the optimal design of energy-absorbing composite tubes.

Recently, there has been a growing interest in replacing synthetic fibres with natural fibres in polymer composites due to environmental concerns. This study examined the fibres from the Newbouldia laevis plant for their potential use in lightweight polymer composites, particularly in applications sensitive to strength and temperature. The fibres were extracted from the plant's stem, and various properties such as density, moisture content, moisture regain, and diameter were measured. Chemical analysis revealed the percentages of cellulose, hemicellulose, lignin, extractives, and ash present in the fibres. Furthermore, Fourier transform infrared analysis confirmed the presence of these essential components. Scanning electron microscopy images showed the rough surfaces of the fibres, which enhance the adhesion between the fibre and matrix during the production of polymer composites. Energy dispersive X-ray analysis identified carbon and oxygen as the main elements in the fibres. Thermal analysis provided insights into the thermal stability and maximum degradation temperatures of the fibres. Lastly, a single fibre tensile test was performed to evaluate the tensile strength, elastic modulus, and elongation at break of the fibres using Weibull distribution statistical analysis. The results of this study indicate that Newbouldia laevis fibres could be a promising reinforcement for lightweight polymer composites in strength and temperature-sensitive applications.

Mountainous areas face challenges such as rugged topography, harsh weather, and limited access to power grids; however, they also offer potential for renewable energy generation, mainly through solar and wind resources. This study aims to evaluate the feasibility of implementing renewable energy systems in these regions and identify the most studied renewable technologies in high mountain contexts using the PRISMA methodology for rigorous literature selection and VOSviewer for bibliometric analysis. Among them, solar photovoltaic and wind energy stand out due to their high potential in these environments. The study analyzes key parameters such as technological efficiency, solar radiation variability, and wind patterns, including technical aspects like minimum wind speeds derived from the Weibull distribution and solar irradiance levels necessary for system design. The results show that the insights obtained from the bibliometric analysis help evaluate the feasibility and performance of renewable energy solutions in complex terrains. In conclusion, the study highlights the most viable technologies for high mountain areas and provides recommendations for their implementation. Although technical and environmental challenges persist, these ecosystems offer significant opportunities for sustainable energy generation. The findings provide guidance for future research and the development of innovative projects in remote, mountainous regions.

Evaluation of statistical methods to study flexural strength of dental CAD-CAM composites.

Multi-criteria group decision-making using ambiguous sets, Weibull distribution, and aggregation operators: A case study in optimal vendor selection for office supplies

This paper presents a highly accurate statistical modeling method for selecting prediction functions for turbine-scale wind energy resources in Bangladesh, which are not yet extensively covered in existing literature. The proposed resource modeling encompasses key turbine parameters, including turbine power, energy pattern factors, and wind energy outputs. The study, surveyed by the United Nations (UN), identifies prospective areas in mainland and coastal belt regions with the capacity to support commercial wind energy conversion systems for modeling purposes. As Bangladesh's current meteorological measurement facilities primarily collect low-elevation data (approximately 10 meters), the study employs prediction models and projection laws to estimate energy resources suitable for turbines of low-to-medium ratings (80 meters, less than 1 MW). The time-series probability distribution of available wind resources is analyzed in these potential regions, utilizing wind velocity prediction functions such as Weibull, Rayleigh, and Gumbel distributions. Their performances are compared against established statistical standards. Weibull factors are derived using graphical least squares (GLS) and modified maximum likelihood (MML) methods, and validated against parameter values reported in the literature. To enhance the analysis's coverage and accuracy, the Weibull function is expanded by incorporating the effect of output power ratio into its probability distribution. Wind power density (WPD) trends are confirmed through energy pattern factors, and a portable wind system model is employed to estimate the actual energy output at prospective locations, thereby increasing the comprehensiveness of the energy data modeling process.

Type II Half-Logistic Rayleigh Weibull distribution with statistical inference and applications

Reliability life analysis of reinforced concrete structures (RCS) in salt corrosion environments: An application of the three-parameter Weibull distribution model

On fitting disability in Saudi Arabia and radiation data: Using the alpha power transformed Rayleigh inverted Weibull distribution

The growing demand for recyclable and high-performance insulation materials in power cable systems has motivated the search for alternatives to cross-linked polyethylene (XLPE). This study investigates the potential of polypropylene (PP)/polyethylene (PE)-based polymer blends, modified with ethylene propylene diene monomer (EPDM), as viable substitutes for traditional insulation in low and medium voltage cables. Thirteen different polymer blend formulations were synthesized and characterized using tensile testing, with statistical analysis performed via the two-parameter Weibull distribution to assess the reliability of mechanical performance. Additionally, two processing methods—injection molding only (blending + plasticizing) and a combination of extrusion (blending) and injection molding (plasticizing)—were evaluated for their impact on process durability and processing efficiency. Among these, the blend containing 40% EPDM, 30% PP, and 30% PE, processed by extrusion followed by injection molding, achieved the highest Weibull modulus (37.1), tensile strength (15.8 MPa), and elongation at break (696%). Prototype cables manufactured with this blend were further evaluated by mechanical, thermal aging, water absorption, shrinkage, and electrical resistance tests according to TS IEC 60,502-1. The optimized blend showed superior performance compared to XLPE, PE, and PVC, with electrical resistivity values exceeding 6 × 10 12 Ω·cm and fracture strain values three times higher than commercial sheathing. This is the first study to integrate Weibull statistical analysis with real-scale prototype validation of PP/PE/EPDM blends, demonstrating their scalability as a recyclable and reliable alternative to XLPE in low- and medium-voltage cable insulation.

Principal component analysis of antiseizure medication-induced hostility/aggression and factor analysis of levetiracetam using the food and drug administration adverse event reporting system.

Stress-strength reliability estimation based on probability weighted moments in small sample scenario with three-parameter Weibull distribution

The new generalized Burr X-inverse Weibull distribution with applications to medical and radiation data

Weibull and Pareto distributions are widely used in several areas, including lifetime data modelling and reliability analysis. In real-life practice, these distributions may not capture the various distributional properties of certain datasets. The use of finite mixture models enhanced the performances of these distributions in adaptability and accuracy. This study focused on the Mixture Weibull Pareto (IV) distribution proposed by [1], which has been used in modelling insurance claims, and it showed superior performance as compared with other distributions. The current study applied this distribution to health and environmental datasets. The result showed that the Mixture Weibull Pareto (IV) distribution performed better than other distributions, using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Kolmogorov-Smirnov test (KS test) for the significance of the distribution.

ABSTRACT We conducted a uniaxial compression experiment using a combination of acoustic emission (AE) and digital image correlation (DIC) real-time monitoring to determine the influence of changes in the inclination angle of a double crack with a prefabricated echelon and rock bridge on the mechanical properties and AE signals of sandstone under uniaxial compression. The deterioration of the sample was most significant when the crack inclination was 30°, and that the elastic modulus and peak stress reached a minimum when the rock bridge inclination was 60°. The prefabricated cracks influenced the variation in the AE signals. The combined effect of the rock bridge and fracture inclination angles complicates the rock fracture process, resulting in different changes in the ringing counts and energy release speeds. Normalising the cumulative energy and cumulative ringing count demonstrated that the ringing count primarily reflected the accumulation of damage events and that the energy largely reflected the intensity characteristics of AE events. Finally, a rock-like damage variable expression that fully reflects the damage evolution process of the sample was obtained based on the damage variables of the acoustic generation energy characteristics combined with the probability density function conforming to the Weibull distribution.

ABSTRACT The Weibull and lognormal distributions are two of the most widely used models for analyzing lifetime data. Both share several properties, and for certain parameter ranges, their cumulative distribution functions can appear quite similar. However, choosing the more suitable distribution is essential for accurate inference. When data are subject to censoring, the problem becomes more complex. Here, we assume that the data come either from a Weibull or a lognormal distribution under hybrid censoring (considering both Type‐I and Type‐II hybrid schemes). To discriminate between the two models, we use the ratio (or equivalently, the difference) of their maximized log‐likelihoods (RML). We further employ a recently developed method based on minimum density power divergence estimators (MDPDE) and compare its performance with the classical likelihood approach in both without outliers and with outliers settings, where it shows clear superiority. In addition, a Bayesian decision criterion is implemented and compared with these methods. The asymptotic distribution of the RML statistic is derived to compute the probability of correct selection and to determine the minimum required sample size for reliable discrimination. Simulation studies are carried out to evaluate how well the asymptotic results hold across different sample sizes, censoring levels, and censoring times. The asymptotic approximations perform well even for moderate sample sizes. Finally, we investigate the effect of model misspecification on key reliability measures, including the th quantile, mean residual life, reliability function, and prediction for future failures. A real data set is analyzed to illustrate the proposed methods.

Background Alopecia is a significant adverse effect that profoundly impacts quality of life. Although numerous medications are implicated, the real-world risk profiles across drug classes and patient demographics remain poorly quantified. Objective To identify and characterize drugs associated with alopecia using real-world data from the FDA Adverse Event Reporting System (FAERS). Methods FAERS reports from Q1 2004 to Q4 2024 were analyzed using four disproportionality methods (ROR, PRR, BCPNN, MGPS) to detect signals of drug-alopecia associations. Subgroup analyses were conducted by age, gender, and drug category. Time-to-onset (TTO) was analyzed using the Weibull distribution model. Results A total of 181,838 reports with drug-associated alopecia were identified. The mean age was 53.84 ± 16.28 years, and 76.82% of reports were from females. Oncology medications showed strongest association (37.5%), especially docetaxel (ROR = 70.38). Endocrine (18.8%) and immune system medications (10.9%) were also prominent. The TTO analysis revealed a bimodal distribution, with 40.2% of cases occurring within 30 days and 13.1% manifesting at 240–360 days. Males experienced a significantly shorter onset latency compared to females (108 days vs. 236 days, P < 0.001). Oncology drugs also showed shorter latency than non-oncology agents (198 vs. 308 days, P < 0.001). Notably, comparison with United States prescribing information revealed that 23.4% of high-signal drugs lacked documentation of alopecia in their official labels. Conclusion This large-scale pharmacovigilance study identified 64 drugs with significant alopecia signals, highlighting distinct demographic patterns and latency periods. The findings underscore the need for heightened clinical vigilance, gender-specific monitoring, and updates to labels to better reflect real-world risks.