Search Results (995)

The growing interest in sustainable materials has encouraged the valorization of agro-industrial byproducts for pharmaceutical, nutraceutical, and food applications. This study evaluated yellow pitaya peel powder, obtained via convective and refractance window drying, as a diluent in immediate-release quercetin tablets. The powders were characterized by physicochemical, granulometric, morphological, and surface properties, and compared with conventional excipients, including partially pregelatinized corn starch and spray-dried lactose monohydrate. Refractance window drying improved solubility, flowability, and structural integrity, while convective drying produced finer, more porous particles with lower water activity. Tablets formulated with both powders showed adequate hardness, low friability, and disintegration times under five minutes. All systems achieved complete quercetin release. Kinetic modeling revealed anomalous, matrix-regulated transport, with Weibull and Modified Hill models providing the best fit. Based on these results, pitaya peel powder could be considered a suitable diluent for the development of immediate-release tablets, offering functional performance aligned with sustainable formulation strategies. Full article

The acceptance of generalized distributions has significantly improved over the past two decades. In this paper, we introduce a new generalized distribution: Topp–Leone odd Weibull flexible-G family of distributions (FoD). The new FoD is a combination of two FOD; the Topp–Leone-G and odd Weibull-flexible-G families. The proposed FoD possesses more flexibility compared to the two individual FoD when considered separately. Some selected statistical properties of this new model are derived. Three special cases from the proposed family are considered. The new model exhibits symmetry and long or short tails, and it also addresses various levels of kurtosis. Monte Carlo simulation studies were conducted to verify the consistency of the maximum likelihood estimators. Two real data examples were used as illustrations on the flexibility of the new model in comparison to other competing models. The developed model proved to perform better than all the selected competing models. Full article

This study analyzes the compressive behavior and reliability of Al-Mg-Si (6061) metal matrix composites reinforced with different weight fractions of Al₂O₃ and SiC ceramics and cast with graphite and steel molds. Compression tests were carried out according to ASTM E9 with 0–8 wt.% reinforcement. The mold material significantly influenced the strength due to the cooling rate and interfacial adhesion. A two-parameter Weibull model assessed statistical reliability and extracted the shape (β) and scale (η) parameters using linear regression. Advanced models—lifelines (frequentist) and Bayesian models—were also applied. Graphite molds yielded composites with higher shape parameters (β = 6.27 for Al₂O₃; 5.49 for SiC) than steel molds (β = 4.66 for Al₂O₃; 4.79 for SiC). The scale values ranged from 490–523 MPa. The lifelines showed similar trends, with the graphite molds exhibiting higher consistency and scale (ρ = 7.45–9.36, λ = 479.71–517.49 MPa). Bayesian modeling using PyMC provided posterior distributions that better captured the uncertainty. Graphite mold samples had higher shape parameters (α = 6.98 for Al₂O₃; 8.46 for SiC) and scale values of 489.07–530.64 MPa. Bayesian models provided wider reliability limits, especially for SiC steel. Both methods confirmed the Weibull behavior. Lifelines proved to be computationally efficient, while Bayesian analysis provided deeper insight into reliability and variability. Full article

This study develops a thermo-mechanical damage (TMD) model for predicting damage evolution in heterogeneous rock materials after heat treatment. The TMD model employs a Weibull distribution to characterize the spatial heterogeneity of the mechanical properties of rock materials and develops a framework that incorporates thermal effects into a nonlocal gradient damage model, thereby overcoming the mesh dependency issue inherent in homogeneous local damage models. The model is validated by numerical simulations of a notched cruciform specimen subjected to combined mechanical and thermal loading, confirming its capability in thermo-mechanical coupled scenarios. Sensitivity analysis shows increased material heterogeneity promotes localized, X-shaped shear-dominated failure patterns, while lower heterogeneity produces more diffuse, network-like damage distributions. Furthermore, the results demonstrate that thermal loading induces micro-damage that progressively spreads throughout the specimen, resulting in a significant reduction in both overall stiffness and critical strength; this effect becomes increasingly pronounced at higher heating temperatures. These findings demonstrate the model’s ability to predict the mechanical behavior of heterogeneous rock materials under thermal loading, offering valuable insights for safety assessments in high-temperature geotechnical engineering applications. Full article

As part of this study, we design a reliability acceptance sampling plan under the assumption that the lifetime of a product follows the Topp–Leone generated Weibull (TLGW) distribution, a model that exhibits structural symmetry in its hazard rate behavior and distributional form. The fundamental procedures for constructing such a plan are described. We compute and tabulate the minimum sample sizes required for given risk criteria using both binomial and Poisson models for the number of failures. We also provide the operating characteristic (OC) values for the proposed sampling plans, and determine the minimum ratios of true mean life to specified mean life needed to satisfy a given producer’s risk. The role of symmetry in the TLGW distribution is highlighted in its balanced tail properties and shape characteristics, which influence the performance of the acceptance sampling plan. Finally, we illustrate the applicability of the proposed plan with real-world data. Full article

Economic dispatch using wind power plants plays a role in reducing the price of electricity production by dispatching power among different generating units for thermal and wind power plants, and supplying load demand while meeting the power system equality and inequality constraints. Adding wind power plants to the economic dispatch model can significantly reduce electricity production costs and reduce carbon dioxide emissions. In this paper, fuel cost and emission minimization are considered as the objective function of the economic dispatch problem, taking into account transmission loss using the B matrix. The quadratic model of the fuel cost and emission criterion functions is modeled without considering a valve-point loading effect. The real power generation limits for both wind and conventional generating units are considered. In addition, a closed-form expression based on the incomplete gamma function is provided to define the impact of wind power, which includes the cost of wind energy, including overestimation and underestimation of available wind power using a Weibull-based probability density function. In this research work, Lagrange’s algorithm is proposed to solve the Wind–Thermal Economic Emission Dispatch (WTEED) problem. The developed Lagrange classical optimization algorithm for the WTEED problem is validated using the IEEE test systems with 6-, 10-, and 40-generation unit systems. The proposed Lagrange optimization method for WTEED problem solutions demonstrates a notable improvement in both economic and environmental performance compared to other heuristic optimization methods reported in the literature. Specifically, the fuel cost was reduced by an average of 4.27% in the IEEE 6-unit system, indicating more economical power dispatch. Additionally, the emission cost was lowered by an average 22% in the IEEE 40-unit system, reflecting better environmental compliance and sustainability. These results highlight the effectiveness of the proposed approach in achieving a balanced trade-off between cost minimization and emission reduction, outperforming several existing heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) under similar test conditions. The research findings report that the proposed Lagrange classical method is efficient and accurate for the convex wind–thermal economic emission dispatch problem. Full article

This study introduces a novel methodology for estimating loading and fatigue damage in the blades of wind turbines, emphasizing non-Gaussian wind conditions’ impact. By calculating blade loading and fatigue using higher statistical moments of the irregular winds, the study demonstrates the significance of non-Gaussian effects on loading and fatigue predictions. A two-step methodology is developed to synthesize non-Gaussian wind processes, integrating the TurbSim (version 1.5) and Hermite moment model transformation methods. These wind time histories are then utilized in a fully coupled simulation of a floating wind turbine, integrating with a blade beam model. Preliminary analysis of wind thrust and the blade root bending moment indicates non-Gaussian effects on aerodynamic loading. Further analysis of fatigue reveals that fatigue hot spots vary along the blade surface, depending on short-term wind conditions and long-term wind distribution, with total fatigue life estimated by summing the fatigue damage at each potential hot spot. The probability density function of long-term wind process is estimated by fitting the Weibull distribution to measured buoy data. The results show that variations in long-term wind speed distributions lead to an average fatigue life difference of about 1.3 years (16%). The Gaussian wind model overestimates fatigue life by roughly 1.5 years (18%) compared to the non-Gaussian model. This highlights the importance of considering both long-term wind distributions and short-term wind characteristics for accurate fatigue assessment. The findings provide valuable insights for the design and operation of floating offshore wind turbines. Full article

Micro and small wind turbines (MAWTs) are increasingly integrated into residential and prosumer hybrid energy systems. However, their real-world performance often falls short of catalog specifications due to mismatched wind resources, siting limitations, and insufficient attention to human comfort. This paper presents a comprehensive decision-support framework for selecting the type and scale of MAWTs under actual local conditions. The energy assessment module combines aerodynamic performance scaling, wind speed-frequency modeling based on Weibull distributions, turbulence intensity adjustments, and component-level efficiency factors for both horizontal and vertical axis turbines. The framework addresses three key design objectives: efficiency—aligning turbine geometry and control strategies with local wind regimes to maximize energy yield; comfort—evaluating candidate designs for noise emissions, shadow flicker, and visual impact near buildings; and climate adaptation—linking turbine siting, hub height, and rotor type to terrain roughness, turbulence, and built environment characteristics. Case studies from low and moderate wind locations in Central Europe demonstrate how multi-criteria filtering avoids oversizing, improves the autonomy of hybrid PV–wind systems, and identifies configurations that may exceed permissible limits for noise or flicker. The proposed methodology enables evidence-based deployment of MAWTs in decentralized energy systems that balance technical performance, resilience, and occupant well-being. Full article

Background: The encapsulation of hydrophilic drugs within microparticles has gained significant interest in drug delivery systems due to their potential to improve stability, bioavailability, and controlled release of therapeutic agents. Biotin, a water-soluble vitamin, presents challenges such as rapid degradation and limited membrane permeability, which constrain its therapeutic effectiveness. Objectives: This study aims to develop and characterize biotin-loaded microparticles formulated with alginate, Eudragit^® E100, and CaCl₂, and to evaluate their characterization and potential applications. Methods: The microparticles were produced using the external ionic gelation method, where alginate and CaCl₂ solutions were mixed under probe sonication. Eudragit^® E100 was added as a complexing agent. The optimized formulation was used to encapsulate biotin, and various experimental variables were screened to study their influence on the properties of the microparticles. Results: Biotin was encapsulated in alginate microparticles (size: 634 nm; polydispersity index: 0.26; zeta potential: −45 mV) with an encapsulation efficiency of 90.5%. In vitro release studies using vertical diffusion Franz cells demonstrated a controlled release profile following the Weibull kinetic model. Conclusions: Encapsulation techniques offer a promising approach to overcome the limitations of hydrophilic drug delivery. The biotin-loaded microparticles developed in this study have potential applications in both topical and oral formulations, providing controlled release and improved therapeutic efficacy, and illustrate the broader applicability of polymeric encapsulation systems for improving the delivery of labile, hydrophilic bioactives. Full article

The combined influence of specimen size and strain rate on the mechanical behaviour of small-diameter bamboo culms remains insufficiently characterised. This study investigates the longitudinal compressive strength of Chimonobambusa utilis through axial compression tests on specimens measuring 15 × 15 × 5 mm, 18 × 18 × 6 mm, and 21 × 21 × 7 mm under strain rates of 10⁻⁴, 10⁻³, and 10⁻² s⁻¹. Coupling experimental data with theoretical analysis, this study develops a size–strain rate interaction model to quantitatively assess the effects of specimen size and strain rate on the compressive strength of small-diameter bamboo. Increasing specimen size reduced strength and shifted failure modes from shear to buckling and splitting. At a strain rate of 10⁻⁴ s⁻¹, strength decreased from 73.35 MPa for the 15 × 15 × 5 mm specimens to 62.84 MPa for the 21 × 21 × 7 mm specimens. Conversely, increasing the strain rate from 10⁻⁴ s⁻¹ to 10⁻² s⁻¹ for the 15 × 15 × 5 mm specimens increased strength from 73.35 MPa to 80.27 MPa, indicating suppressed crack propagation. The Type II Weibull model exhibited higher predictive accuracy and parameter stability than the Type I variant. Coupling the Type II Weibull function with a power-law strain rate term and an interaction exponent developed a predictive equation, achieving relative errors below 5%. The findings demonstrate that specimen size predominantly governs strength, whereas strain rate exerts a secondary but enhancing influence. The proposed coupling model enables reliable axial load prediction for small-diameter bamboo culms, supporting material selection and dimensional optimisation in structural applications. Full article

Peeled hard-cooked eggs (HCEs) are a popular and convenient choice for consumers and food servicers. A recent outbreak and several recalls of HCEs have highlighted their susceptibility to contamination with Listeria monocytogenes. HCEs are generally treated with antimicrobials, such as citric acid, to enhance the safety and quality of the product. A 2019 multistate outbreak linked to consumption of contaminated HCEs in the U.S. prompted research on the effectiveness of citric acid, and other organic acids, to control L. monocytogenes on this food product. This study therefore assessed the use of organic acids as antimicrobials against L. monocytogenes on HCEs. HCEs were dip-inoculated with L. monocytogenes, resulting in an initial concentration of ca. 8 log CFU/HCE. Following air-drying for 10 min, HCEs were treated at 5 or 25 °C with water, 0.3 or 2% citric acid, or 2% acetic, lactic, or malic acid for up to 24 h to determine reductions in L. monocytogenes populations. After 24 h of treatment, 0.3% citric acid treatment resulted in population reductions of <1.24 log CFU/HCE regardless of treatment temperature, while 2% organic acids resulted in statistically significant reductions of 2.88–4.78 log CFU/HCE at 5 °C and 2.35–5.10 log CFU/HCE at 25 °C. The highest L. monocytogenes reductions on HCEs resulted from the 2% malic acid treatment at 5 °C and the 2% acetic acid treatment at 25 °C. Primary modeling was used to determine the inactivation kinetics and model fit with the Weibull and log-linear models, both estimating the rapid rates of inactivation when using the 2% malic and lactic acid treatments at 25 °C. The results of this study suggest that 2% acetic, lactic, and malic acids may be effective treatments for the control of L. monocytogenes on HCEs. Full article

The growing concern about climate change and increased carbon emissions has promoted the electric vehicle market. Lithium-Ion Batteries (LIBs) are now the prevailing technology in electromobility, and large amounts will soon reach their end-of-life (EoL). Most counties have not designed sustainable reverse logistics networks to collect, remanufacture and recycle EoL electric vehicle batteries (EVBs). This study is focused on estimating the future EoL LIBs generation through dynamic material flow analysis using a three parameter Weibull distribution function under two scenarios for battery lifetime and then designing a reverse logistics network for the region of Attica (Greece), based on a generalizable modeling framework, to handle the discarded batteries up to 2040. The methodology considers three different battery handling strategies such as recycling, remanufacturing, and disposal. According to the estimated LIB waste generation in Attica, the designed network would annually manage between 5300 and 9600 tons of EoL EVBs by 2040. The optimal location for the collection and recycling centers considers fixed costs, processing costs, transportation costs, carbon emission tax and the number of EoL EVBs. The economic feasibility of the network is also examined through projected revenues from the sale of remanufactured batteries and recovered materials. The resulting discounted payback period ranges from 6.7 to 8.6 years, indicating strong financial viability. This research underscores the importance of circular economy principles and the management of EoL LIBs, which is a prerequisite for the sustainable promotion of the electric vehicle industry. Full article

Industrial systems often rely on specialized redundant systems to enhance reliability and prevent unexpected failures. This study introduces a novel three-parameter model, the Poisson–Weibull distribution (PWD), and discovers its various key properties. The primary focus of the study is to develop stress–strength (SS) model based on this newly developed distribution. Parameter estimation for both the PWD and SS models is carried out using maximum likelihood estimation (MLE) and Bayesian estimation techniques. Given the complexity of the proposed distribution, numerical approximation techniques are employed to obtain reliable parameter estimates. A comprehensive simulation study employing the Monte Carlo simulation (MCS) and Markov Chain Monte Carlo (MCMC) examines the behavior of the PWD and SS model parameters under various scenarios. The development of the SS model enhances understanding of the PWD’s dynamics while providing practical insights into its real-life applications and limitations. The effectiveness of the proposed distribution and the SS reliability measure is established through applications to real-life data sets. Full article

The generalized Pareto distribution is frequently used for modeling extreme values above an appropriate threshold level. Since the process of determining the appropriate threshold value is difficult, a mixture of extreme value models rises to prominence. In this study, mixture extreme value models based on exponentiated Pareto distribution are proposed. The Weibull, gamma, and log-normal models are used as bulk densities. The parameter estimates of the proposed models are obtained using the maximum likelihood approach. Two different approaches based on maximization of the log-likelihood and Kolmogorov–Smirnov p-value are used to determine the appropriate threshold value. The effectiveness of these methods is compared using simulation studies. The proposed models are compared with other mixture models through an application study on earthquake data. The GammaEP web application is developed to ensure the reproducibility of the results and the usability of the proposed model. Full article

Many scholars are interested in modeling complex data in an effort to create novel probability distributions. This article proposes a novel class of distributions based on the inverse of the exponentiated Weibull hazard rate function. A particular member of this class, the Weibull–Rayleigh distribution (WR), is presented with focus. The WR features diverse probability density functions, including symmetric, right-skewed, left-skewed, and the inverse J-shaped distribution which is flexible in modeling lifetime and systems data. Several significant statistical features of the suggested WR are examined, covering the quantile, moments, characteristic function, probability weighted moment, order statistics, and entropy measures. The model accuracy was verified through Monte Carlo simulations of five different statistical estimation methods. The significance of WR is demonstrated with three real-world data sets, revealing a higher goodness of fit compared to other competing models. Additionally, the change point for the WR model is illustrated using the modified information criterion (MIC) to identify changes in the structures of these data. The MIC and curve analysis captured a potential change point, supporting and proving the effectiveness of WR distribution in describing transitions. Full article