Search Results (128)

This work proposes a novel extension for a new extended Weibull distribution. Some statistical properties of the proposed distribution are studied including quantile, moments, skewness, and kurtosis. The hazard rate function of the new distribution has certain elastic qualities, allowing it to take increasing, upside-down bathtub, and modified upside-down bathtub shapes commonly observed in medical contexts. Different methods of estimation are studied using complete data. Two real data sets from the medical field are analyzed to demonstrate that the proposed model has adaptability in practice. In comparison to some well-known distributions, the suggested distribution fits the tested data better based on both parametric and non-parametric statistical criteria. A simulation study is presented to compare the obtained estimates based on mean square error and average absolute bias. 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

Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for continuous data analysis named after authors Aadil Ajaz and Parvaiz. The proposed family effectively accommodates both symmetric and asymmetric characteristics through its shape-controlling parameter, an essential feature for capturing diverse data patterns. A specific subclass of this family, termed the “AAP Exponential” (AAPEx) model is designed to address the inflexibility of classical exponential distributions by accommodating versatile hazard rate patterns, including increasing, decreasing and bathtub-shaped patterns. Several fundamental mathematical characteristics of the introduced family are derived. The model parameters are estimated using six frequentist estimation approaches, including maximum likelihood, Cramer–von Mises, maximum product of spacing, ordinary least squares, weighted least squares and Anderson–Darling estimation. Monte Carlo simulations demonstrate the finite-sample performance of these estimators, revealing that maximum likelihood estimation and maximum product of spacing estimation exhibit superior accuracy, with bias and mean squared error decreasing systematically as the sample sizes increases. The practical utility and symmetric–asymmetric adaptability of the AAPEx model are validated through five real-world applications, with special emphasis on cancer survival times, COVID-19 mortality rates and reliability data. The findings indicate that the AAPEx model outperforms established competitors based on goodness-of-fit metrics such as the Akaike Information Criteria (AIC), Schwartz Information Criteria (SIC), Akaike Information Criteria Corrected (AICC), Hannan–Quinn Information Criteria (HQIC), Anderson–Darling (A*) test statistic, Cramer–von Mises (W*) test statistic and the Kolmogorov–Smirnov (KS) test statistic and its associated p-value. These results highlight the relevance of symmetry in real-life data modeling and establish the AAPEx family as a powerful tool for analyzing complex data structures in public health, engineering and epidemiology. Full article

Coastal zones represent the most active interfaces where natural processes and human activities converge, making them crucial for biodiversity and socioeconomic development. These zones are characterized by their fragility and susceptibility to frequent natural disasters, such as floods and erosion, which are exacerbated by high-intensity human activities and urban expansion. The ongoing challenges posed by rising sea levels and climate change necessitate robust scientific assessments of coastal vulnerability to ensure effective disaster prevention and environmental protection. This paper introduces a comprehensive evaluation system for assessing coastal zone vulnerability, utilizing multi-source data to address ecological vulnerabilities stemming from sea-level rise and climate change impacts. This system is applied to examine the specific case of Qatar, where rapid urban development and a high population density in coastal areas heighten the risk of flooding and inundation. Employing remote sensing data and Geographic Information Systems (GISs), this research leverages spatial interpolation techniques and high-resolution digital elevation models (DEMs) to identify and evaluate high-risk zones susceptible to sea-level rise. In this study, the hydrological connectivity model, bathtub technique, and CVI are interconnected tools that complement each other to assess future flooding risks under various climate change projections, highlighting the increased probability of coastal hazards. The findings underscore the urgent need for adaptive planning and regulatory frameworks to mitigate these risks, providing technical support for the sustainable development of coastal communities globally and in Qatar. This approach not only informs policy makers, but also aids in the strategic planning required to foster resilient coastal infrastructure capable of withstanding both current and future environmental challenges. Full article

The growing demand for high-power battery output in the ever-evolving electric vehicle and energy storage sectors necessitates the development of efficient thermal management systems. High-power lithium-ion batteries (LIBs), known for their outstanding performance, are widely used across various applications. However, effectively managing the thermal conditions of high-power battery packs remains a critical challenge that limits the operational efficiency and hinders broader market acceptance. The high charge and discharge rates in LIBs generate significant heat, and, as a result, inadequate heat dissipation adversely impacts battery performance, lifespan, and safety. This study utilized theoretical analysis, numerical simulations, and experimental methodologies to address these issues. Considering the anisotropic heat transfer characteristics of laminated pouch cells, this study developed a fluid–solid coupling simulation model tailored to the liquid-cooled structure of pouch battery modules, supported by an experimental test setup. A U-shaped “bathtub-type” cooling structure was designed for a 48 V/8 Ah high-power-density battery pack intended for start–stop power supply applications. This design aimed to resolve heat dissipation challenges, optimize the cooling efficiency, and ensure stable operation under varying conditions. During the performance assessments of the cooling structure conducted through simulations and experiments, extreme discharge conditions (320 A) and pulse charging/discharging cycles (80 A) at ambient temperatures of up to 45 °C were simulated. An analysis of the temperature distribution and its temporal evolution led to critical insights. The results showed that, under these severe conditions, the maximum temperature of the battery module remained below 60 °C, with temperature uniformity maintained within a 5 °C range and cell uniformity within 2 °C. Consequently, the battery pack meets the operational requirements for start–stop power supply applications and provides an effective solution for thermal management in high-power-density environments. Full article

The increasing complexity of modern lifetime data necessitates the development of more flexible probability models. To address this need, we propose the power length-biased new XLindley (PLNXL) distribution, a novel two-parameter model tailored to model a wide range of lifetime datasets. Characterized by both shape and scale parameters, the PLNXL distribution effectively captures diverse hazard rate functions, including increasing, decreasing, and inverted bathtub-shaped forms. Additionally, its mean residual life function is capable of exhibiting decreasing, increasing, and bathtub-shaped behaviors, thereby enhancing its practical relevance. We derive key mathematical properties of the distribution, including moments, reliability measures, and entropy. The parameters are estimated using the maximum likelihood method, and simulation studies confirm the consistency and efficiency of the estimators. The applicability of the proposed model is illustrated using real-world datasets, where it consistently outperforms the existing models. These results highlight the robustness and adaptability of the PLNXL distribution for lifetime data analysis across a wide array of applications. Full article

The weighted flexible Weibull distribution focuses on its unique point of flaunting a bathtub-shaped hazard rate, characterized by an initial increase followed by a drop over time. This property plays a major role in reliability analysis. In this paper, this distribution and its main properties are examined, and the parameters are estimated using several estimation methods. In addition, a simulation study is done for different sample sizes. The performance of the proposed model is illustrated through two real-world applications: component failure times and COVID-19 mortality. Moreover, the value-at-risk (VaR), tail value-at-risk (TVaR), peaks over a random threshold VaR (PORT-VaR), the mean of order P (${\mathrm{MOP}}_{\left[P\right]}$) analysis, and optimal order of P due to the true mean value can help identify and characterize critical events or outliers in failure events and COVID-19 death data across different counties. Finally, the PORT-VaR estimators are provided under a risk analysis for both applications. Full article

A novel odd generalized exponential Kumaraswamy–Weibull distribution is defined. This distribution is distinguished by its capacity to capture a wider class of hazard functions than the standard Weibull models, such as non-monotonic and bathtub-shaped hazards. This is an advancement in distribution theory because it provides a new simplified form of the distribution with a much more complicated behavior, which results in better statistical inference and detail in survival analysis and other related fields. Considerations on the identifiability of the proposed distribution are addressed, emphasizing the distinct contributions of its parameters and their roles in model behavior characterization. One real dataset from a survival experiment is considered, highlighting the practical implications of our distribution in the context of reliability. Full article

Rational waste management is crucial for the effective implementation of the circular economy (CE) and the achievement of Sustainable Development Goals (SDGs). Ceramic waste, which takes thousands of years to decompose in the natural environment, can be recycled into construction materials. This approach offers dual environmental benefits: reducing ceramic waste disposal and minimizing the exploitation of natural aggregate deposits. This study examines the recycling of sanitary ceramic waste, including items such as washbasins, toilet bowls, urinals, bidets, and bathtubs, into alternative aggregates for concrete mixtures. After grinding and separating the ceramic cullet into specific fractions, it becomes a viable substitute for natural aggregates. Concrete samples were tested with varying water-cement ratios (0.3 and 0.4) and recycled ceramic aggregate contents (15%, 30%, and 45%). These results were compared to those of samples made solely with natural aggregates. The samples underwent compressive strength tests to determine concrete class and were exposed to elevated temperatures (150 °C, 300 °C, 550 °C, and 750 °C). Additional analyses measured the secant modulus of elasticity and selected aggregate properties. The findings demonstrate that high-quality concrete can be produced while promoting circular economy principles by reducing waste and preserving natural resources. Full article

This study develops a new method for generating families of distributions based on the alpha power transformation and the trigonometric function, which enables enormous versatility in the resulting sub-models and enhances the ability to more accurately characterize tail shapes. This proposed family of distributions is characterized by a single parameter, which exhibits considerable flexibility in capturing asymmetric datasets, making it a valuable alternative to some families of distributions that require additional parameters to achieve similar levels of flexibility. The sine alpha power generated family is introduced using the proposed method, and some of its members and properties are discussed. A particular member, the sine alpha power-Weibull (SAP-W), is investigated in depth. Graphical representations of the new distribution display monotone and non-monotone forms, whereas the hazard rate function takes a reversed J shape, J shape, bathtub, increasing, and decreasing shapes. Various characteristics of SAP-W distribution are derived, including moments, rényi entropies, and order statistics. Parameters of SAP-W are estimated using the maximum likelihood technique, and the effectiveness of these estimators is examined via Monte Carlo simulations. The superiority and potentiality of the proposed approach are demonstrated by analyzing three real-life engineering applications. The SAP-W outperforms several competing models, showing its flexibility. Additionally, a novel-log location-scale regression model is presented using SAP-W. The regression model’s significance is illustrated through its application to real data. Full article

The Hjorth distribution is more flexible in modeling various hazard rate shapes, including increasing, decreasing, and bathtub shapes. This makes it highly useful in reliability analysis and survival studies, where different failure rate behaviors must be captured effectively. In some practical experiments, the observed data may appear to be continuous, but their intrinsic discreteness requires the development of specialized techniques for constructing discrete counterparts to continuous distributions. This study extends this methodology by discretizing the Hjorth distribution using the survival function approach. The proposed discrete Hjorth distribution preserves the essential statistical characteristics of its continuous counterpart, such as percentiles and quantiles, making it a valuable tool for modeling lifetime data. The complexity of the transformation requires numerical techniques to ensure accurate estimations and analysis. A key feature of this study is the incorporation of Type-II censored samples. We also derive key statistical properties, including the quantile function and order statistics, and then employ maximum likelihood and Bayesian inference methods. A comparative analysis of these estimation techniques is conducted through simulation studies. Furthermore, the proposed model is validated using two real-world datasets, including electronic device failure times and ball-bearing failure analysis, by applying goodness-of-fit tests against alternative discrete models. The findings emphasize the versatility and applicability of the discrete Hjorth distribution in reliability studies, engineering, and survival analysis, offering a robust framework for modeling discrete data in practical scenarios. To our knowledge, no prior research has explored the use of censored data in analyzing discrete Hjorth-distributed data. This study fills this gap, providing new insights into discrete reliability modeling and broadening the application of the Hjorth distribution in real-world scenarios. Full article

The Cox proportional hazards model is one of the most popular regression models for censored survival data. In the Cox model, the baseline hazard function is often modeled by cubic spline functions. However, the penalized likelihood estimation for fitting cubic spline models is computationally challenging. In this paper, we propose a computationally simple approach to implement the cubic spline model without penalizing the likelihood. The proposed method consists of two stages under the five-parameter spline model. The first stage estimates a scale parameter for a given shape model. The second stage adopts a model selection from 13 candidate shape models. We implement the proposed methods in our new R package “splineCox” (version 0.0.3) and it has been made available in CRAN. We conduct simulation studies to assess the performance of the proposed method. To illustrate the advantage of the proposed model, we analyze a life test dataset on electrical insulations and a gene expression dataset on lung cancer patients. Full article

This paper introduces a new continuous lifetime model called the Odd Flexible Weibull-Weibull (OFW-W) distribution, which features three parameters. The new model is capable of modeling both symmetric and asymmetric datasets, regardless of whether they are positively or negatively skewed. Its hazard rate functions can exhibit various behaviors, including increasing, decreasing, unimodal, or bathtub-shaped. The key characteristics of the OFW-W model are discussed, including the quantile function, median, reliability and hazard rate functions, kurtosis and skewness, mean waiting (residual) lifetimes, moments, and entropies. The unknown parameters of the model are estimated using eight different techniques. A comprehensive simulation study evaluates the performance of these estimators based on bias, mean squared error (MSE), and mean relative error (MRE). The practical usefulness of the OFW-W distribution is demonstrated through four real datasets from the fields of engineering and medicine, including complete data, upper record data, and type-II right-censored data. Comparisons with five other lifetime distributions reveal that the OFW-W model exhibits superior flexibility and capability in fitting various data types, highlighting its advantages and improvements. In conclusion, we anticipate that the OFW-W model will prove valuable in various applications, including human health, environmental studies, reliability theory, actuarial science, and medical sciences, among others. Full article

Tropical cyclones (TCs) are natural hazards causing extensive damage to society, infrastructure, and the natural environment. Due to the multi-hazardous nature of TCs, comprehensive risk assessments are essential to understanding how to better prepare for potential impacts. This study develops an integrated methodology for TC multi-hazard risk assessment that utilises the following individual assessments of key TC risk components: a variable enhanced bathtub model (VeBTM) for storm surge-driven hazards, a random forest (RF) machine learning model for rainfall-induced flooding, and indicator-based indices for exposure and vulnerability assessments. To evaluate the methodology, the regions affected by TC Debbie (2017) for Queensland and TC Winston (2016) for Fiji’s main island of Viti Levu were used as proof-of-concept case studies. The results showed that areas with the highest risk of TC impacts were close to waterbodies, such as at the coastline and along riverine areas. For the Queensland study region, coastal populated areas showed levels of “high”, “very high”, and “extreme” risk, specifically in Bowen and East Mackay, driven by the social and infrastructural domains of TC risk components. For Viti Levu, areas classified with an “extreme” risk to TCs are primarily areas that experienced coastal inundation, with Lautoka and Vuda found to be especially at risk to TCs. Additionally, the Fiji case study was validated using post-disaster damage data, and a statistically significant correlation of 0.40 between TC Winston-attributed damage and each tikina’s overall risk was identified. Ultimately, this study serves as a prospective framework for assessing TC risk, capable of producing results that can assist decision-makers in developing targeted TC risk management and resilience strategies for disaster risk reduction. Full article

This study investigates the use of participatory geographic information systems (PGIS) for hazard assessment in small island developing states (SIDS), with a focus on spatial literacy and community-based disaster management. By partnering with the Lavongai community on Papua New Guinea, this research aimed to empower community members through skill development in geodata processing. The program leveraged local knowledge and the global positioning system to create participatory maps, enhancing both community capacity and researcher data quality. Workshops and focus group discussions (FGDs) were conducted to assess the community’s understanding of spatial concepts related to disaster risks. The core objective was a preliminary assessment of the community’s social and economic vulnerability to coastal disasters, using household data and GIS analysis. The results showed varied vulnerability levels within the community, highlighting the need for targeted disaster mitigation training and nature-based solutions. High-resolution satellite imagery and a simple bathtub model simulated sea level rise, identifying land-uses at risk. The program concluded with a community presentation of thematic maps, fostering collaboration and transparency. Future projects will address environmental challenges identified by local leaders and prioritize skill development, social data collection, and water resource mapping. Full article