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Green technologies are defined as the utilization of advanced scientific and technological methodologies to fabricate products that minimize environmental impact. The assessment of green technology alternatives necessitates a comprehensive analysis incorporating a multitude of criteria, many of which may be conflicting. Optimal selections must encompass technical performance, economic feasibility, environmental sustainability, and societal implications. Additionally, the data gaps and vague information typical when dealing with emerging technologies make traditional techniques unproductive. This work thus proposes a dynamic multi-criteria group decision making (MCGDM) model by integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method with the Evaluation based on Distance from Average Solution (EDAS) technique under the linguistic T-spherical fuzzy (LT-SF) environment. Initially, we define some Hamacher operations for LT-SF numbers (LT-SFNs) and then use them to develop some Hamacher aggregation operators (HAOs) synthesizing expert assessments. Meanwhile, some prominent features of these newly developed operators are also discussed. Next, we introduce a novel LT-SF-CRITIC-EDAS model, where LT-SF-CRITIC determines criteria weights, and LT-SF-EDAS evaluates the ranking of available alternatives. To illustrate the designed model’s applicability, we apply it to a real-world scenario of selecting the most appropriate green technology from available options. Finally, a sensitivity analysis and comparative evaluation against existing methods demonstrate our proposed approach’s superior feasibility and reliability. This research contributes to advancing decision making methodologies for assessing green technologies under complex and uncertain conditions.

Bounded random variables arise naturally in physical, engineering, and reliability systems when measurements represent proportions, efficiencies, normalized intensities, or constrained state variables. In this paper, a flexible bounded stochastic framework generated through a beta transformation of the Kumaraswamy (Kw) baseline is introduced, yielding a four-parameter family capable of capturing diverse boundary behaviors and hazard rate (HR) structures. Rigorous theoretical properties of the proposed model are developed, including structural identifiability, limiting behavior at the boundaries, shape characteristics of the probability density function (PDF) and HR functions, and explicit stochastic representations. Closed-form expressions for moments, probability-weighted moments are derived under mild regularity conditions, together with comprehensive information-theoretic characterizations based on Shannon, Rényi, and Tsallis entropies, as well as Kullback–Leibler divergence relative to baseline models. Likelihood-based inference is studied in detail, with explicit score functions, Fisher information, and asymptotic properties of the maximum likelihood estimators (MLE) established. An illustrative application to bounded measurements from an engineered system demonstrates the practical relevance of the theoretical results. The proposed framework provides a mathematically rigorous and interpretable tool for uncertainty quantification and reliability analysis of bounded physical quantities.

This article uses upper record values to estimate the stress-strength reliability parameter, defined as We assume that both strength ( T ) and stress (Z) are independent random variables that follow the inverted exponentiated Pareto distribution with a common second shape parameter. The maximum likelihood and Bayesian estimators of are obtained. Using informative and non-informative priors, the Bayesian estimators are obtained under symmetric and asymmetric loss functions. Two bootstrap-type confidence intervals and highest posterior credible intervals are constructed. Gibbs and Metropolis-Hasting samplers are used to generate Bayesian estimates of reliability based on the suggested loss functions. To investigate the behavior of suggested approaches, extensive simulation studies are carried out using some accuracy measures. Simulation experiment findings validated the consistency of the Bayesian and non-Bayesian estimates of According to specific metrics, Bayesian estimates under symmetric loss function showed more precision than those under asymmetric loss functions. The lengths of credible intervals for Bayesian estimates are less than the bootstrap confidence intervals for different record numbers. The bootstrap-p confidence intervals give more accurate outcomes than bootstrap-t in most cases. The analysis employs two representative datasets. The first includes the timing of goals scored in the final rounds of the European Champions League over two consecutive seasons. The second dataset contains monthly observations of sulfur dioxide concentration in Long Beach, California, spanning the years 1956 to 1974.

The modified Weibull model (MWM) is one of the type-2 Weibull distributions that can be used for modeling lifetime data. It is important due to its simplicity and flexibility of the failure rate, and ease of parameter estimation using the least squares method. In this study, we introduce novel methods for estimating the parameters in step-stress partially accelerated life testing (SSPALT) in the context of progressive Type-II censoring (PT-II) under Constant-Barrier Removals (CBRs) for the MWM. We conduct a comparative analysis between Expectation Maximization (EM) and Stochastic Expectation Maximization (SEM) techniques with Bayes estimators under Markov Chain Monte Carlo (MCMC) methods. Specifically, we focus on Replica Exchange MCMC, the Hamiltonian Monte Carlo (HMC) algorithm, and the Riemann Manifold Hamiltonian Monte Carlo (RMHMC), emphasizing the use of the Linear Exponential (LINEX) loss function. Additionally, highest posterior density (HPD) intervals derived from the RMHMC sampler consistently outperform asymptotic and bootstrap confidence intervals, providing the shortest credible regions while maintaining nominal coverage across all censoring levels and stress conditions. A comprehensive Monte Carlo simulation study is conducted to assess the performance of these methods. Furthermore, the proposed methodology is applied to a real dataset comprising lifetimes of electrical appliances, demonstrating the practical effectiveness of the MWM in modeling real-world reliability data. Results show that the Bayesian RMHMC approach offers superior accuracy and convergence properties.

This study introduces the Odd-Exponential-Ailamujia (OEA) distribution, a novel extension of the Ailamujia distribution via the T-X family, offering enhanced flexibility for modeling complex lifetime data in reliability and survival analysis. Key statistical properties, including moments, moment-generating function, characteristic function, mean residual life, and mean waiting time, are derived using binomial and Taylor series expansions, transforming intractable integrals into computable forms and enabling precise approximation of distributional behavior. The hazard rate function exhibits diverse shapes (increasing, decreasing, or unimodal), controlled by parameters, making the proposed model adaptable to varied failure patterns. Applied to aircraft windshield failure data, the OEA distribution demonstrates superior fit over competing models through goodness-of-fit tests, various plots of reliability measures, 3D surface interactions, and heatmaps revealing parameter-driven correlations. Efficient Python implementation to ensures scalable inference. The OEA distribution emerges as a robust, versatile tool for reliability engineering, survival modeling, and probabilistic forecasting, effectively capturing real-world failure dynamics.

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters , , and . These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

Since traffic congestion is a major issue worldwide, particularly in urban areas due to the increasing population on a daily basis, it negatively impacts human life, leading to time wastage, health problems, and economic repercussions. Therefore, establishing a sustainable transportation system in densely populated urban areas is crucial. To overcome this challenge, various factors on which a sustainable transportation system depends must be deeply analyzed. However, this analysis may involve fuzziness and vagueness due to imprecise data and incomplete information. In this regard, a mathematical framework called the Complex Intuitionistic Fuzzy Hypersoft Set (CIFHSS) is introduced in this research. This article presents and thoroughly investigates essential set-theoretical operations based on the complex intuitionistic fuzzy hypersoft set, using appropriate examples. Furthermore a novel Multi-Attribute Decision Making approach, based on the CIFHSS is developed. This approach incorporates decision-valued matrices (both maximum and minimum), a scoring framework for CIFHSS, and matrix-based aggregations, such as the core matrix. Moreover, the algorithm is implemented in a real-world scenario to solve traffic congestion in urban areas, demonstrating the versatility of the proposed algorithm. The model assigns scores of 0.6161 to the Electric Bus Rapid Transit system, 0.6089 to the Expanded Bicycle Lane Network, 0.6571 to Light Rail Transit, and 0.7627 to the Hybrid Car-Sharing Program, identifying the latter as the most suitable option. A sensitivity analysis of the proposed model is also presented using statistical tools. Finally, a detailed conclusion and future directions for the application of this research are provided.

Triple modular redundancy (TMR) is a robust technique utilized in safety-critical applications to enhance fault-tolerance and reliability. This article focuses on estimating the distribution parameters of a TMR system under step-stress partially accelerated life tests, where each component included in the system follows a Lomax distribution. The study aims to analyze the system’s reliability and mean residual lifetime based on the estimated parameters. Various estimation techniques, including maximum likelihood, percentile, least squares, and maximum product of spacings, are explored. Additionally, the optimal stress change time is determined using two criteria. An illustrative example supported by two actual data sets is presented to showcase the methodology’s application. By conducting Monte Carlo simulations, the assessment of the estimation methods’ effectiveness reveals that the maximum likelihood method outperforms the other three methods in terms of both accuracy and performance, as indicated by the numerical outcomes. This research contributes to the understanding and practical implementation of TMR systems in safety-critical industries, potentially saving lives and preventing catastrophic events.

This study investigates the impact of outliers on the evolution of clusters in temporal data-sets. Monitoring and tracing cluster transitions of temporal data sets allow us to observe how clusters evolve and change over time. By tracking the movement of data points between clusters, we can gain insights into the underlying patterns, trends, and dynamics of the data. This understanding is essential for making informed decisions and drawing meaningful conclusions from the clustering results. Cluster evolution refers to the changes that occur in the clustering results over time due to the arrival of new data points. The changes in cluster solutions are classified as external and internal transitions. The study employs the survival ratio and history cost function to investigate the effects of outliers on changes experienced by the clusters at successive time points. The results demonstrate that outliers have a significant impact on cluster evolution, and appropriate outlier handling techniques are necessary to obtain reliable clustering results. The findings of this study provide useful insights for practitioners and researchers in the field of stream clustering and can help guide the development of more robust and accurate stream clustering algorithms.