Search Results (53)

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

Wireless sensor networks (WSNs) are widely used in IoT, environmental monitoring, and industrial systems, but ensuring energy efficiency, extended network lifetime, and reliable communication under real-world constraints remains challenging. This work proposes a novel clustering framework that integrates kernel density estimation (KDE)-based adaptive node deployment, silhouette-optimized K-means clustering, Bayesian cluster head (CH) selection with Gaussian noise-based energy uncertainty modeling, energy-efficient coverage control, and carrier sense multiple access with collision avoidance-based data transmission. Unlike conventional approaches that rely on fixed clustering and uniform deployments, our framework supports terrain-aware node placement and dynamic CH selection based on residual energy and distance under imperfect sensing conditions. Simulation results demonstrate significant improvements in performance, including over 35% extension in network lifetime and higher coverage retention under energy constraints, compared to baseline methods such as LEACH and K-LEACH. While detailed metrics vary per run due to adaptive parameters and stochastic node behavior, these outcomes validate the scalability, robustness, and practical relevance of the proposed method for real-world WSN deployments. Full article

This study proposes a generalized family of distributions to enhance flexibility in modeling complex engineering and biomedical data. The framework unifies existing models and improves reliability analysis in both engineering and biomedical applications by capturing diverse system behaviors. We introduce a novel hybrid family of distributions that incorporates a flexible set of hybrid functions, enabling the extension of various existing distributions. Specifically, we present a three-parameter special member called the hybrid-Weibull–exponential (HWE) distribution. We derive several fundamental mathematical properties of this new family, including moments, random data generation processes, mean residual life (MRL) and its relationship with the failure rate function, and its related asymptotic behavior. Furthermore, we compute advanced information measures, such as extropy and cumulative residual entropy, and derive order statistics along with their asymptotic behaviors. Model identifiability is demonstrated numerically using the Kullback–Leibler divergence. Additionally, we perform a stress–strength (SS) reliability analysis of the HWE under two common scale parameters, supported by illustrative numerical evaluations. For parameter estimation, we adopt the maximum likelihood estimation (MLE) method in both density estimation and SS-parameter studies. The simulation results indicated that the MLE demonstrates consistency in both density and SS-parameter estimations, with the mean squared error of the MLEs decreasing as the sample size increases. Moreover, the average length of the confidence interval for the percentile and Student’s t-bootstrap for the SS-parameter becomes smaller with larger sample sizes, and the coverage probability progressively aligns with the nominal confidence level of 95%. To demonstrate the practical effectiveness of the hybrid family, we provide three real-world data applications in which the HWE distribution outperforms many existing Weibull-based models, as measured by AIC, BIC, CAIC, KS, Anderson–Darling, and Cramer–von Mises criteria. Furthermore, the HLW exhibits strong performance in SS-parameter analysis. Consequently, this hybrid family holds immense potential for modeling lifetime data and advancing reliability and survival analysis. Full article

Lightweight and high-strength alloys such as Al and Ti alloys are commonly employed materials for aviation structural components. A “hole-fastener” is commonly used for their connection, and DMCE (direct mandrel cold expansion) is a reliable technique in industries to enhance the fatigue properties of hole-involved components due to its advantages, i.e., convenient, efficient and cost-effective. However, an inadequate understanding of the DMCE process leads to a vast amount of waste in industries when any materials or structural parameters are changed. In order to promote the application efficiency of the DMCE process in aviation industries and reduce the energy and resource waste caused by repeated attempts, taking Al7050 and TB6 as examples, this paper comprehensively investigates the fatigue enhancement mechanism of the DMCE process on lightweight and high-strength alloys. Numerical models with 12.9%, 36.9% residual stress prediction errors and 9.98%, 14.8% radial plastic deformation prediction errors for Al and Ti holes were established, and then simulations were performed to screen out five significant influence parameters from eleven independent parameters. On this basis, DMCE experiments with significant parameters were carried out, and the improvement mechanisms of the DMCE process on the tangential residual stress, radial plastic deformation and surface morphology of Al and Ti hole walls were comparatively analyzed. Furthermore, fatigue life prediction models for two-hole-involved specimens were generated via multiple linear regression, which exhibit, respectively, 13.5% and 33.9% mean prediction errors for Al and Ti alloys. Moreover, the optimal DMCE schemes were obtained and 2.33 and 4.12 times fatigue lifetime improvements were achieved for the Al and the Ti specimens. 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

This paper introduces a novel energy-efficient lightweight, void hole avoidance, localization, and trust-based scheme, termed as Energy-Efficient and Trust-based Autonomous Underwater Vehicle (EETAUV) protocol designed for 6G-enabled underwater acoustic sensor networks (UASNs). The proposed scheme addresses key challenges in UASNs, such as energy consumption, network stability, and data security. It integrates a trust management framework that enhances communication security through node identification and verification mechanisms utilizing normal and phantom nodes. Furthermore, a 6G communication module is deployed to reduce network delay and enhance packet delivery, contributing to more efficient data transmission. Leveraging Autonomous Underwater Vehicles (AUVs), the EETAUV protocol offers a lightweight approach for node discovery, identification, and verification while ensuring a high data transmission rate through a risk-aware strategy including at low computational cost. The protocol’s performance is evaluated through extensive simulations and compared against state-of-the-art methods across various metrics, including network lifetime, throughput, residual energy, packet delivery ratio, mean square error, routing overhead, path loss, network delay, trust, distance, velocity, Computational Cost of Routing, and data security. The results demonstrate the superior cumulative performance of the proposed EETAUV scheme, making it a robust solution for secure, efficient, and reliable communication in UASNs. Full article

This paper addresses the issue of noise interference and variable working conditions in the production and machining environment, which lead to weak tool life features and reduced prediction accuracy. A tool lifetime prediction method based on AVMD-DAMResNet is proposed. The method first adapts the parameters of the variational modal noise reduction algorithm using an improved sparrow optimization algorithm, and then reconstructs the original vibration signal with noise reduction. Second, the residual module of the deep residual network is enhanced using a two-dimensional attention mechanism. A parallel residual network tool prediction model (DAMResNet) was constructed to optimize the model’s weight allocation to different features, achieving multi-channel and multi-dimensional feature fusion. Finally, the noise-reduced signal was input into the DAMResNet model to accurately predict tool lifetime. The experimental results show that, compared with the original ResNet model, the proposed AVMD-DAMResNet model improves the coefficient of determination (R²) by 5.8%, reduces the root mean square error (RMSE) by 31.2%, and decreases the mean absolute percentage error (MAPE) by 31.4%. These results demonstrate that the AVMD-DAMResNet-based tool lifetime prediction method effectively reduces noise and achieves high prediction accuracy. Full article

In this paper the exponentiated generalized xgamma distribution is introduced. Some of its properties are presented through some models of stress–strength, moments, mean residual life, mean past lifetime, and order statistics. The maximum likelihood estimators, confidence intervals for the parameters, and the reliability and the hazard rate functions of the exponentiated generalized xgamma distribution based on dual generalized order statistics are obtained. Bayesian estimators for the unknown parameters, reliability, and hazard rate functions of the exponentiated generalized xgamma distribution based on dual generalized order statistics are considered. The results based on lower record values are verified using simulations as well as three real sets of data are adopted to demonstrate the flexibility and potential applications of the distribution. Full article

In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection. Full article

The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a widely used method for managing energy consumption in Wireless Sensor Networks (WSNs). However, it has limitations that affect network longevity and performance. This paper presents an improved version of the LEACH protocol, termed MFG-LEACH, which incorporates the Mean Field Game (MFG) theory to optimize energy efficiency and network lifetime. The proposed MFG-LEACH protocol addresses the imbalances in energy consumption by modeling the interactions among nodes as a game, where each node optimizes its transmission energy based on the collective state of the network. We conducted extensive simulations to compare MFG-LEACH with Enhanced Zonal Stable Election Protocol (EZ-SEP), Energy-Aware Multi-Hop Routing (EAMR), and Balanced Residual Energy routing (BRE) protocols. The results demonstrate that MFG-LEACH significantly reduces energy consumption and increases the number of packets received across different node densities, thereby validating the effectiveness of our approach. Full article

Various discrete lifetime distributions have been observed in real data analysis. Numerous discrete models have been derived from a continuous distribution using the survival discretization method, owing to its simplicity and appealing formulation. This study focuses on the discrete analog of the newly generalized Rayleigh distribution. Both classical and Bayesian statistical inferences are performed to evaluate the efficacy of the new discrete model, particularly in terms of relative bias, mean square error, and coverage probability. Additionally, the study explores different important submodels and limiting behavior for the new discrete distribution. Various statistical functions have been examined, including moments, stress–strength, mean residual lifetime, mean past time, and order statistics. Finally, two real data examples are employed to evaluate the new discrete model. Simulations and numerical analyses play a pivotal role in facilitating statistical estimation and data modeling. The study concludes that the discrete generalized Rayleigh distribution presents a notably appealing alternative to other competing discrete distributions. Full article

The total loss lubrication system that is typical of chainsaws is responsible for a massive dispersion in the agro-forestry environment of highly impactful pollutants, mostly of fossil origin, often well known as carcinogenic substances, which, in addition to presenting a risk to the environment, represent an important risk factor for human health, especially for chainsaw users. During its use, the chain lubricant is dispersed from the guide bar tip in the form of droplets and aerosol, or it is adsorbed on wood residues and sawdust. Then, it is subjected to drift, settles on the ground and vegetation, and can hit the operators, who, after prolonged exposures, can suffer both irritation of the respiratory tract and dermal absorption. Such a risk factor is often amplified by the widespread use of less-expensive, sometimes illegal alternatives, such as exhausted motor oils. To mitigate said negative effects, a process has been in progress for several years that is aimed at replacing conventional lubricants with synthetic or biobased oils with increasing biodegradability. As a contribution to this process, a study has been started on the possibility of using refined olive pomace oil (ROPO) as a base stock for the formulation of a totally biodegradable chainsaw lubricant. On purpose, to improve its properties of viscosity and adhesivity, such an oil was added with a biodegradable thickening agent, obtaining four formulations with different viscosity. After a lab test and a preliminary cutting test on firewood, the formulation with 2% of thickener resulted in being the best, and 3.0 g kg⁻¹ of tert-butylhydroquinone (TBHQ), a food-grade antioxidant, was then added to form the final formulation (F₂) to be compared, in the subsequent four test sessions, to a biodegradable commercial chain lubricant (S_B). The tests were carried out without changing the chainsaw setting, on different wood species, both in forest and, with the aim of increasing the repeatability of tests conditions and comparability of results, at a fixed point. The fluids’ performances were mainly evaluated based both on the operators’ opinions and on the measurements of the chain–bar temperatures and of saw chain wear related to a predefined number of cuts. As to the destiny of the fluid dispersed during cutting, the overall dispersion was assessed by considering the average working time, the consumption of chain lubricant, and the forest area cut down daily. Eventually, the amounts of inhalable and respirable dust particles as vectors of oil residues were quantified by means of personal air samplers worn by the operators and analyzed to determine any differences in the concentration of metallic elements. The test results evidenced chain temperatures that were 0.5, 4.9, and 12.5 °C higher with F₂ relating to S_B, respectively, in the cutting of trunks of fresh Pinus, Eucalyptus, and dry Pinus. They were accompanied by chain weight losses of 89.5% and 35% higher with F₂ relating to S_B, respectively, in cutting tests of Turkey oak and Poplar. Such a greater wear, however, apparently did not affect the saw chain’s cutting efficiency with F₂, since the operators declared that they did not notice any difference between the performances of the two fluids at the time of comparison. The effects of higher wear on the chain lifetime, any deriving risks for the operator’s safety, and the possibility to reduce the wear levels observed with F₂ will be explored in a further study, e.g., through different settings of the lubricating system of the chainsaw. The results of the analyses of the air-sampled dust residues that were evidenced with F₂ showed lower concentrations of respirable and inhalable particles and of some metallic elements (Al, Mg, and Ca) than those with S_B. This behavior probably depends on the different interaction between sawdust and the two fluids, which differ according to their chemical–physical characteristics (different viscosity, composition, and additives). However, it represents a positive factor in favor of the use of the ROPO-based lubricant, emphasized by the total biodegradability of its residues that are possibly contained in the dust inhaled by the operators. Full article

Network lifetime and localization are critical design factors for a number of wireless sensor network (WSN) applications. These networks may be randomly deployed and left unattended for prolonged periods of time. This means that node localization is performed after network deployment, and there is a need to develop mechanisms to extend the network lifetime since sensor nodes are usually constrained battery-powered devices, and replacing them can be costly or sometimes impossible, e.g., in hostile environments. To this end, this work proposes the energy-aware connected k-neighborhood (ECKN): a joint position estimation, packet routing, and sleep scheduling mechanism. To the best of our knowledge, there is a lack of such integrated solutions to WSNs. The proposed localization algorithm performs trilateration using the positions of a mobile sink and already-localized neighbor nodes in order to estimate the positions of sensor nodes. A routing protocol is also introduced, and it is based on the well-known greedy geographic forwarding (GGF). Similarly to GGF, the proposed protocol takes into consideration the positions of neighbors to decide the best forwarding node. However, it also considers node residual energy in order to guarantee the forwarding node will deliver the packet. A sleep scheduler is also introduced in order to extend the network lifetime. It is based on the connected k-neighborhood (CKN), which aids in the decision of which nodes switch to sleep mode while keeping the network connected. An extensive set of performance evaluation experiments was conducted and results show that ECKN not only extends the network lifetime and localizes nodes, but it does so while sustaining the acceptable packet delivery ratio and reducing network overhead. Full article

In the present work, we study the combined m-consecutive-k-out-of-n: F and k_c-out-of-n: F reliability systems, which consist of independent and identically distributed components. Two different redundancy policies are considered, and their general frameworks are described and illustrated. The main objective of the paper refers to the investigation of the effect of adding cold standby redundancy to the system at the the system level and the component level. Exact formulae for determining the crucial characteristics of the enhanced structure, such as its survival function, the mean time to failure and the mean residual lifetime, are provided. All formulae proved in the present manuscript are explicit expressions which are based on the signature vector of the resulting reliability schemes. An extensive numerical investigation is carried out to shed light on the performance of the combined m-consecutive-k-out-of-n: F and consecutive k_c-out-of-n: F reliability systems with cold standby redundancy. Some concluding remarks and comments are provided upon the determination of the optimal design parameters. Full article

In this article, using the scaled (weighted) residual life variable, some scaled measures, the scaled mean residual life and the scaled hazard rate, are introduced. Several scales are considered as examples of the derivation of the scaled measures. The measures are developed for the case of a weighted residual life at a random time, and it is shown that the measures are scale-free in these cases. This property proves useful in situations where a relative comparison of the lifetime distribution is studied. Some characterization properties are derived in terms of scaled measures evaluated at some sequences of random time points that follow a typical distribution. Examples are used to illustrate, examine, and satisfy the obtained characterizations. Full article