Search Results (110)

Hydrodynamic cavitation is one of the most promising technologies for sustainable process intensification in the food, nutraceutical, and environmental sectors, due to its ability to generate highly localized and intense implosions. Venturi-type devices, known for their simplicity and efficiency, are widely used for non-thermal extraction, microbial inactivation, and cellular disruption. However, the effectiveness of cavitation critically depends on internal geometry—particularly the perimeter-to-area ratio (P/A), which influences both pressure gradient distribution and the density of nucleation sites. In this context, an innovative configuration based on the Reuleaux triangle is proposed, allowing for a significant increase in the P/A ratio compared to conventional circular-section devices. This theoretical study extends the Navier–Stokes and Rayleigh–Plesset models to describe bubble dynamics and assess the influence of geometric and rotational variants (VRAt) on the localization and intensity of cavitation collapse. The results suggest that optimized internal geometries can reduce treatment times, increase selectivity, and improve the overall energy efficiency of cavitation processes, offering strong potential for advanced and sustainable industrial applications. This work is entirely theoretical and is intended to support the future design and experimental validation of next-generation cavitating devices. Full article

This paper introduces the record-based transmuted Rayleigh distribution of order 3 (rbt-R), a three-parameter extension of the classical Rayleigh model designed to address data characterized by high skewness and heavy tails. While traditional generalizations of the Rayleigh distribution enhance model flexibility, they often lack sufficient adaptability to capture the complexity of empirical distributions encountered in applied statistics. The rbt-R model incorporates two additional shape parameters, a and b, enabling it to represent a wider range of distributional shapes. Parameter estimation for the rbt-R model is performed using the maximum likelihood method. Simulation studies are conducted to evaluate the asymptotic properties of the estimators, including bias and mean squared error. The performance of the rbt-R model is assessed through empirical applications to four datasets: nicotine yields and carbon monoxide emissions from cigarette data, as well as breaking stress measurements from carbon-fiber materials. Model fit is evaluated using standard goodness-of-fit criteria, including AIC, AIC_c, BIC, and the Kolmogorov–Smirnov statistic. In all cases, the rbt-R model demonstrates a superior fit compared to existing Rayleigh-based models, indicating its effectiveness in modeling highly skewed and heavy-tailed data. Full article

The Rayleigh distribution is a continuous probability distribution that is inherently asymmetric and commonly used to model right-skewed data. It holds significant importance across a wide range of scientific and engineering disciplines and exhibits structural relationships with several other asymmetric probability distributions, for example, Weibull and exponential distribution. This research proposes techniques for establishing credible intervals and confidence intervals for the single mean of the zero-inflated two-parameter Rayleigh distribution. The study introduces methods such as the percentile bootstrap, generalized confidence interval, standard confidence interval, approximate normal using the delta method, Bayesian credible interval, and Bayesian highest posterior density. The effectiveness of the proposed methods is assessed by evaluating coverage probability and expected length through Monte Carlo simulations. The results indicate that the Bayesian highest posterior density method outperforms the other approaches. Finally, the study applies the proposed methods to construct confidence intervals for the single mean using real-world data on COVID-19 total deaths in Singapore during October 2022. Full article

This paper investigates an understudied generalization of the classical exponential, Rayleigh, and Weibull distributions, known as the power generalized Weibull distribution, particularly in the context of censored data. Characterized by one scale parameter and two shape parameters, the proposed model offers enhanced flexibility for modeling diverse lifetime data patterns and hazard rate behaviors. Notably, its hazard rate function can exhibit five distinct shapes, including upside-down bathtub and bathtub shapes. The study focuses on classical and Bayesian estimation frameworks for the model parameters and associated reliability metrics under a unified hybrid censoring scheme. Methodologies include both point estimation (maximum likelihood and posterior mean estimators) and interval estimation (approximate confidence intervals and Bayesian credible intervals). To evaluate the performance of these estimators, a comprehensive simulation study is conducted under varied experimental conditions. Furthermore, two empirical applications on real-world cancer datasets underscore the efficacy of the proposed estimation methods and the practical viability and flexibility of the explored model compared to eleven other existing lifespan models. Full article

The end conditions of columns constitute an important design parameter as they change their stiffness. The degree of restraint of the column modifies its fundamental frequency and mode of vibration. The rotational stiffness at its ends may transform from zero (hinged) to infinite (clamped). For intermediate values, the rotational movement is partially restricted, and it is classified as semi-rigid. In this work, the seismic response for a linearly variable section column and with gradual change in the rotational fixity is studied. A parametric solution is developed using the Rayleigh method, derived for cases of non-prismatic columns, and considering the axially distributed force along the column height. The obtained generalized stiffness and mass are used to perform approximate seismic evaluation at low effort and examine the influence of the changes to the structure. The analysis indicated that with a spring coefficient of 5 EI/l, the displacement drops by 50%, meaning that this range can produce significant influence on the structural response. The relationship between the top load and the column self-weight equal to 0.3 defines the limit for the hinged–hinged boundary condition to exist. As research recommendations, analysis of columns with variable cross-sections and different shapes, different distributed loadings, applying the rotational spring for both ends and over the shape functions, and analysis of buildings by an equivalent system are suggested. Experimental activity is indicated as a possibility for future investigations. Full article

Wind energy is a clean, inexhaustible resource with significant potential to reduce coal dependence, lower carbon emissions, and provide sustainable energy in the off-grid areas of South Africa’s Eastern Cape. However, due to wind variability, site-specific assessments are crucial for accurate resource estimation and investment risk mitigation. This study evaluates the wind energy potential at Fort Hare using six statistical distribution models: Weibull (WEI), Rayleigh (RAY), gamma (GAM), generalized extreme value (GEV), inverse Gaussian (IGA), and Gumbel (GUM). The analysis is based on three years (2021–2023) of hourly wind speed data at 10 m above ground level from the Fort Beaufort weather station. Parameters were estimated using the maximum likelihood method (MLM), and model performance was ranked using the total error (TE) metric. The results indicate an average wind speed of 2.60 m/s with a standard deviation of 1.85 m/s. The GEV distribution was the best fit (TE = 0.020), while the widely used Weibull distribution ranked third (TE = 0.5421), highlighting its limitations in capturing wind variability and extremes. This study underscores the importance of testing multiple models for accurate wind characterization and suggests improving the performance of the Weibull model through advanced parameter optimization, such as artificial intelligence. The wind power density was 31.52 W/m², classifying the site as poor for large-scale electricity generation. The prevailing wind direction was southeast. Recommendations include deploying small-scale turbines and exploring augmentative systems to optimize wind energy utilization in the region. Full article

With the demand for energy gravimetric and volumetric density in electrical vehicles, lithium-ion batteries are undergoing a trend toward larger formats, along with maximized cell-to-pack efficiency. Current battery thermal management systems and battery modeling, relying on point measurement (thermocouples/thermistors), face challenges in providing comprehensive characterization for larger batteries and extensive monitoring across the pack. Here, we proposed a novel Rayleigh-scattering-based distributed optical fiber sensor to deliver thermal images of a large prismatic cell. Using an optical fiber of 1 mm diameter wrapped around the cell, the optical sensor delivered over 400 unique measurement locations at 3 mm spatial resolution. During a 1.0 C charge, the optical-measured maximum temperature difference was 8.2 °C, while point-like thermocouples, located at the cell front surface and rear surface center, only had a 0.8 °C maximum temperature difference. Moreover, the all-surface-covered optical sensor identified hotspot generation around the vicinity of the tabs, highlighting the essential role of tabs. The maximum temperature on the negative current tab reached 113.9 °C during a 1.5 C discharge, while the hottest spot on the cell surface was only 52.1 °C. This was further validated by the operando thermal image in both the time domain and the spatial domain, facilitating a detailed analysis of the thermal-behavior-like heat generation on the current tabs, transmission through the surface, and dissipation to the cell bottom. Full article

This paper aims to examine the physical layer security (PLS) performance of a reconfigurable intelligent surface (RIS)-aided wiretap multiple-input single-output (MISO) system over generalized fading conditions by assuming inherent phase shift errors at the RIS. Specifically, the procedures (i.e., the method) to conduct this research is based on learning-based approaches to model the magnitude of the end-to-end RIS channel, i.e., employing an unsupervised expectation-maximization (EM) approach via a finite mixture of Nakagami-m distributions. This general framework allows us to accurately approximate key practical factors in RIS’s channel modeling, such as generalized fading conditions, spatial correlation, discrete phase shift, beamforming, and the presence of direct and indirect links. For the numerical results, the secrecy outage probability, the average secrecy rate, and the average secrecy loss under different setups of RIS-aided wireless systems are assessed by varying the fading parameters of the N-wave with a diffuse power fading channel model. The results show that the correlation between RIS elements and unfavorable channel conditions (e.g., Rayleigh) affect secrecy performance. Likewise, it was confirmed that the use of a RIS is not essential when there is a solid line-of-sight link between the transmitter and the legitimate receiver. Full article

Diode-pumped multi-mode graded-index (GRIN) fiber Raman lasers provide prominent brightness enhancement both in linear and half-open cavities with random distributed feedback via natural Rayleigh backscattering. Femtosecond laser-inscribed random refractive index structures allow for the sufficient reduction in the Raman threshold by means of Rayleigh backscattering signal enhancement by +50 + 66 dB relative to the intrinsic fiber level. At the same time, they offer an opportunity to generate Stokes beams with a shape close to fundamental transverse mode (LP₀₁), as well as to select higher-order modes such as LP₁₁ with a near-1D longitudinal random structure shifted off the fiber axis. Further development of the inscription technology includes the fabrication of 3D ring-shaped random structures using a spatial light modulator (SLM) in a 100/140 μm GRIN multi-mode fiber. This allows for the generation of a multi-mode diode-pumped GRIN fiber random Raman laser at 976 nm with a ring-shaped output beam at a relatively low pumping threshold (~160 W), demonstrated for the first time to our knowledge. Full article

This study investigates the statistical inference of the parameters, reliability function, and hazard function of the generalized Rayleigh distribution under progressive first-failure censoring samples, considering factors such as long product lifetime and challenging experimental conditions. Firstly, the progressive first-failure model is introduced, and the maximum likelihood estimation for the parameters, reliability function, and hazard function under this model are discussed. For interval estimation, confidence intervals have been constructed for the parameters, reliability function, and hazard function using the bootstrap method. Next, in Bayesian estimation, considering informative priors and non-information priors, the Bayesian estimation of the parameters, reliability function, and hazard function under symmetric and asymmetric loss functions is obtained using the MCMC method. Finally, Monte Carlo simulation is conducted to compare mean square errors, evaluating the superiority of the maximum likelihood estimation and Bayesian estimation under different loss functions. The performance of the estimation methods used in the study is illustrated through illustrative examples. The results indicate that Bayesian estimation outperforms maximum likelihood estimation. Full article

The proliferation of solar panel installations presents significant societal and environmental advantages. However, many panels are situated in remote or inaccessible locations, like rooftops or vast desert expanses. Moreover, monitoring individual panel performance in large-scale systems poses a logistical challenge. Addressing this issue necessitates an efficient surveillance system leveraging wide area networks. This paper introduces an Internet of Sensing Things (IoST)-based monitoring system integrated with sun-tracking capabilities for solar panels. Cutting-edge sensors and microcontrollers collect real-time data and securely store it in a cloud-based server infrastructure, enabling global accessibility and comprehensive analysis for future optimization. Innovative techniques are proposed to maximize power generation from sunlight radiation, achieved through continuous panel alignment with the sun’s position throughout the day. A solar tracking mechanism, utilizing light-dependent sensors and servo motors, dynamically adjusts panel orientation based on the sun’s angle of elevation and direction. This research contributes to the advancement of efficient and sustainable solar energy systems. Integrating state-of-the-art technologies ensures reliability and effectiveness, paving the way for enhanced performance and the widespread adoption of solar energy. Additionally, the paper explores anomaly prediction using Rayleigh distribution, offering insights into potential irregularities in solar panel performance. Full article

This paper explores the likelihood and Bayesian estimation of the stress–strength reliability parameter ($\mathcal{R}$) based on a lower record ranked set sampling scheme from the generalized Rayleigh distribution. Maximum likelihood and Bayesian estimators as well as confidence intervals of $\mathcal{R}$ are derived and their properties are studied. Furthermore, two parametric bootstrap confidence intervals are introduced in the paper. A comparative simulation study is conducted to assess the effectiveness of these four confidence interval methodologies in estimating $\mathcal{R}$. The application of the methods is demonstrated using real data on fiber strength to showcase their practicability and relevance in the industry. Full article

In this paper, we introduce a new three-parameter inverse Rayleigh distribution that extends the inverse Rayleigh distribution, constructed based on the generalized transmuted family of distributions proposed by Alizadeh, Merovci, and Hamedani. We explore statistical properties such as the quantile function, moments, harmonic mean, mean deviation, stress–strength reliability, and entropy. Parameter estimation is performed using various methods, including maximum likelihood, least squares, the method of the maximum product of spacings, and the method of Cramér–von Mises. The usefulness of the new three-parameter inverse Rayleigh distribution is illustrated by modeling a real dataset, demonstrating its superior fit compared to several other distributions. Full article

In this study, we introduce two novel discrete counterparts for the Rayleigh–Lindley mixture, constructed through the application of survival and hazard rate preservation techniques. These two-parameter discrete models demonstrate exceptional adaptability across various data types, including skewed, symmetric, and monotonic datasets. Statistical analyses were conducted using maximum likelihood estimation and Bayesian approaches to assess these models. The Bayesian analysis, in particular, was implemented with the squared error and LINEX loss functions, incorporating a modified Lwin Prior distribution for parameter estimation. Through simulation studies and numerical methods, we evaluated the estimators’ performance and compared the effectiveness of the two discrete adaptations of the Rayleigh–Lindley distribution. The simulations reveal that Bayesian methods are especially effective in this setting due to their flexibility and adaptability. They provide more precise and dependable estimates for the discrete Rayleigh–Lindley model, especially when using the hazard rate preservation method. This method is a compelling alternative to the traditional survival discretization approach, showcasing its significant potential in enhancing model accuracy and applicability. Furthermore, two real data sets are analyzed to assess the performance of each analog. Full article

High-order cylindrical vector beams possess flexible spatial polarization and exhibit new effects and phenomena that can expand the functionality and enhance the capability of optical systems. However, building a general analytical model for highly focused beams with different polarization orders remains a challenge. Here, we elaborately develop the vector theory of high-order cylindrical vector beams in a high numerical aperture focusing system and achieve the vectorial diffraction integrals for describing the tight focusing field with the space-variant distribution of polarization orders within the framework of Richards–Wolf diffraction theory. The analytical formulae include the exact three Cartesian components of electric and magnetic distributions in the tightly focused region. Additionally, utilizing the analytical formulae, we can achieve the gradient force, scattering force, and curl-spin force exerted on Rayleigh particles trapped by high-order cylindrical vector beams. These results are crucial for improving the design and engineering of the tightly focused field by modulating the polarization orders of high-order cylindrical vector beams, particularly for applications such as optical tweezers and optical manipulation. This theoretical analysis also extends to the calculation of complicated optical vortex vector fields and the design of diffractive optical elements with high diffraction efficiency and resolution. Full article