Search Results (12)

This study focuses on estimating the unknown parameters and the reliability function of the inverted-Weibull distribution, using an improved adaptive progressive Type-II censoring scheme under a competing risks model. Both classical and Bayesian estimation approaches are explored to offer a thorough analysis. Under the classical approach, maximum likelihood estimators are obtained for the unknown parameters and the reliability function. Approximate confidence intervals are also constructed to assess the uncertainty in the estimates. From a Bayesian standpoint, symmetric Bayes estimates and highest posterior density credible intervals are computed using Markov Chain Monte Carlo sampling, assuming a symmetric squared error loss function. An extensive simulation study is carried out to assess how well the proposed methods perform under different experimental conditions, showing promising accuracy. To demonstrate the practical use of these methods, a real dataset is analyzed, consisting of the survival times of male mice aged 35 to 42 days after being exposed to 300 roentgens of X-ray radiation. The analysis demonstrated that the inverted Weibull distribution is well-suited for modeling the given dataset. Furthermore, the Bayesian estimation method, considering both point estimates and interval estimates, was found to be more effective than the classical approach in estimating the model parameters as well as the reliability function. Full article

This study offers a newly improved Type-II adaptive progressive censoring with data sampled from an inverse XLindley (IXL) distribution for more efficient and adaptive reliability assessments. Through this sampling mechanism, we evaluate the parameters of the IXL distribution, as well as its reliability and hazard rate features. In the context of reliability, to handle flexible and time-constrained testing frameworks in high-reliability environments, we formulate maximum likelihood estimators versus Bayesian estimates derived via Markov chain Monte Carlo techniques under gamma priors, which effectively capture prior knowledge. Two patterns of asymptotic interval estimates are constructed through the normal approximation of the classical estimates and of the log-transformed classical estimates. On the other hand, from the Markovian chains, two patterns of credible interval estimates are also constructed. A robust simulation study is carried out to compare the classical and Bayesian point estimation methods, along with the four interval estimation methods. This study’s practical usefulness is demonstrated by its analysis of a real-world dataset. The results reveal that both conventional and Bayesian inferential methods function accurately, with the Bayesian outcomes surpassing those of the conventional method. Full article

This study introduces an objective Bayesian approach for estimating the reliability of a multicomponent stress–strength model based on the Pareto distribution under an adaptive progressive Type-II censoring scheme. The proposed method is developed within a Bayesian framework, utilizing a reference prior with partial information to improve the accuracy of point estimation and to ensure the construction of a credible interval for uncertainty assessment. This approach is particularly useful for addressing several limitations of a widely used likelihood-based approach in estimating the multicomponent stress–strength reliability under the Pareto distribution. For instance, in the likelihood-based method, the asymptotic variance–covariance matrix may not exist due to certain constraints. This limitation hinders the construction of an approximate confidence interval for assessing the uncertainty. Moreover, even when an approximate confidence interval is obtained, it may fail to achieve nominal coverage levels in small sample scenarios. Unlike the likelihood-based method, the proposed method provides an efficient estimator across various criteria and constructs a valid credible interval, even with small sample sizes. Extensive simulation studies confirm that the proposed method yields reliable and accurate inference across various censoring scenarios, and a real data application validates its practical utility. These results demonstrate that the proposed method is an effective alternative to the likelihood-based method for reliability inference in the multicomponent stress–strength model based on the Pareto distribution under an adaptive progressive Type-II censoring scheme. Full article

High-intensity Interval Training (HIIT) is increasingly recognized for enhancing neuromuscular function, strength, power, and overall athletic performance. This review systematically examined peer-reviewed studies published between 2000 and 2025, focusing on HIIT’s impact on motor unit recruitment, muscle fiber composition, neuromuscular efficiency, maximal strength, rate of force development (RFD), muscle hypertrophy, and power output. Findings indicate that HIIT significantly improves neuromuscular activation by increasing motor unit recruitment and synchronization, particularly in fast-twitch fibers essential for explosive movements. HIIT also promotes shifts toward Type II and hybrid Type IIa fibers, enhancing strength and endurance. However, despite its effectiveness in boosting RFD and explosive power, HIIT is less efficient than traditional resistance training for maximizing absolute strength and hypertrophy due to insufficient progressive overload. Integrating resistance-based and plyometric-based HIIT protocols emerged as an effective strategy to enhance strength and power concurrently. Effective periodization and recovery strategies, including active recovery and targeted nutrition, help mitigate fatigue and optimize adaptations. Notable research gaps include the long-term impacts of HIIT on neuromuscular function and the efficacy of individualized HIIT protocols based on specific athlete characteristics. Future research should focus on refining HIIT protocols for different sports, exploring its synergy with traditional resistance training, and assessing long-term adaptations to sustain strength and power gains. HIIT presents a valuable, time-efficient complement to conventional training methods for improving strength, power, and neuromuscular efficiency. Full article

Cardiac rehabilitation, a multi-component intervention designed to mitigate the impact of cardiovascular disease, often underutilises low-intensity resistance exercise despite its potential benefits. This narrative review critically examines the mechanistic and clinical evidence supporting the incorporation of low-intensity resistance exercise into cardiac rehabilitation programmes. Research indicates that low-intensity resistance exercise induces hypertrophic adaptations by maximising muscle fibre activation through the size principle, effectively recruiting larger motor units as it approaches maximal effort. This activation promotes adaptation in both type I and II muscle fibres, resulting in comparable increases in myofibrillar protein synthesis and phosphorylation of key signalling proteins when compared to high-intensity resistance exercise. Low-intensity resistance exercise provides equivalent improvements in muscular strength and hypertrophy compared to high-intensity protocols while addressing barriers to participation, such as concerns about safety and logistical challenges. By facilitating engagement through a more accessible exercise modality, low-intensity resistance exercise might improve adherence rates and patient outcomes in cardiac rehabilitation. Additionally, the ability of low-intensity resistance exercise to address sarcopenia and frailty syndrome, significant determinants of cardiovascular disease progression, can enhance the recovery and overall quality of life for patients. This review establishes evidence-based recommendations for the inclusion of low-intensity resistance exercise in cardiac rehabilitation, offering a promising pathway to enhance the effectiveness of these programmes. Full article

Hematological malignancies are among the top five most frequent forms of cancer in developed countries worldwide. Although the new therapeutic approaches have improved the quality and the life expectancy of patients, the high rate of recurrence and drug resistance are the main issues for counteracting blood disorders. Chemotherapy-resistant leukemic clones activate molecular processes for biological survival, preventing the activation of regulated cell death pathways, leading to cancer progression. In the past decade, leukemia research has predominantly centered around modulating the well-established processes of apoptosis (type I cell death) and autophagy (type II cell death). However, the development of therapy resistance and the adaptive nature of leukemic clones have rendered targeting these cell death pathways ineffective. The identification of novel cell death mechanisms, as categorized by the Nomenclature Committee on Cell Death (NCCD), has provided researchers with new tools to overcome survival mechanisms and activate alternative molecular pathways. This review aims to synthesize information on these recently discovered RCD mechanisms in the major types of leukemia, providing researchers with a comprehensive overview of cell death and its modulation. Full article

To gather enough data from studies that are ongoing for an extended duration, a newly improved adaptive Type-II progressive censoring technique has been offered to get around this difficulty and extend several well-known multi-stage censoring plans. This work, which takes this scheme into account, focuses on some conventional and Bayesian estimation missions for parameter and reliability indicators, where the unit log-log model acts as the base distribution. The point and interval estimations of the various parameters are looked at from a classical standpoint. In addition to the conventional approach, the Bayesian methodology is examined to derive credible intervals beside the Bayesian point by leveraging the squared error loss function and the Markov chain Monte Carlo technique. Under varied settings, a simulation study is carried out to distinguish between the standard and Bayesian estimates. To implement the proposed procedures, two actual data sets are analyzed. Finally, multiple precision standards are considered to pick the optimal progressive censoring scheme. Full article

Recently, a newly improved Type-II adaptive progressive censoring plan was devised, which can successfully ensure that the test length will not surpass a particular threshold period. In this study, we explore the statistical inference of the alpha power exponential distribution in the context of improved adaptive progressive Type-II censored data. The parameters, reliability, and hazard functions were estimated from both classical and Bayesian viewpoints using this censoring plan. To begin, we applied the maximum likelihood estimation approach to obtain parameter, reliability, and hazard function estimators. Following that, the approximate confidence intervals for the aforementioned metrics were derived, assuming the asymptotic normality traits of the maximum likelihood estimators. Additionally, by employing the Bayesian method via the Markov chain Monte Carlo technique, the point estimators and highest posterior density intervals of various parameters were created based on the symmetric squared error loss. A simulation study that incorporates numerous scenarios was used to assess the effectiveness of various estimation methodologies. The optimal progressive censorship plans are then discussed based on a set of criteria. Finally, three applications from the engineering and medical domains have been offered as examples. Full article

This article utilizes improved adaptive progressively Type-II censored data to estimate the entropy of the inverse Weibull distribution. Rényi, q, and Shannon entropy measurements are used to define entropy to achieve this objective. Both point and interval estimations of the entropy quantities are investigated through the maximum likelihood and maximum product of spacing methods. Two parametric bootstrap confidence intervals based on the two estimation techniques are also considered for the various entropy measures. A Monte Carlo simulation study is conducted to investigate how estimates behave at various sample sizes and different censoring schemes based on some statistical measurements. The simulations demonstrate that, as anticipated, when the sample size grows, the estimation accuracy also grows. Furthermore, they show that the estimated entropy measures get closer to the actual entropy values when the censoring level decreases. For purposes of explanation, two applications to actual datasets are taken into consideration. The results verified that the adaptive or improved adaptive progressive censoring schemes give more information about data than the conventional progressive censoring scheme in terms of minimum entropy measures. Full article

A new two-parameter extended exponential lifetime distribution with an increasing failure rate called the Poisson–exponential (PE) model was explored. In the reliability experiments, an adaptive progressively Type-II hybrid censoring strategy is presented to improve the statistical analysis efficiency and reduce the entire test duration on a life-testing experiment. To benefit from this mechanism, this paper sought to infer the unknown parameters, as well as the reliability and failure rate function of the PE distribution using both the likelihood and product of spacings estimation procedures as a conventional view. For each unknown parameter, from both classical approaches, an approximate confidence interval based on Fisher’s information was also created. Additionally, in the Bayesian paradigm, the given classical approaches were extended to Bayes’ continuous theorem to develop the Bayes (or credible interval) estimates of the same unknown quantities. Employing the squared error loss, the Bayesian inference was developed based on independent gamma assumptions. Because of the complex nature of the posterior density, the Markov chain with the Monte Carlo methodology was used to obtain data from the whole conditional distributions and, therefore, evaluate the acquired Bayes point/interval estimates. Via extensive numerical comparisons, the performance of the estimates provided was evaluated with respect to various criteria. Among different competing progressive mechanisms, using four optimality criteria, the best censoring was suggested. Two real chemical engineering datasets were also analyzed to highlight the applicability of the acquired point and interval estimators in an actual practical scenario. Full article

Clinical benefits obtained from checkpoint blockade regimens demonstrate the importance of overcoming the immunosuppressive tumour microenvironment (TME) in cancer immunotherapy. Intravenous (i.v.) injection of B16 melanoma cells (H-2K^b) leads to lethal disseminated pulmonary metastasis in Balb/c recipients (H-2K^d). This lack of immune control is related to low major histocompatibility complex (MHC) expression on B16 cells which is associated with delayed and decreased anti-tumour adaptive immune responses (e.g., alloantibody formation) as: (i) other tumour types with normal H-2K^b expression are rejected with concomitant antibody production; (ii) preincubation of B16 with IFN-gamma to upregulate H-2K^b expression resulted in improved antibody production and anti-tumour activity. The delayed/decreased anti-tumour adaptive immune responses induced by B16 inoculation is not able to interrupt progression of primary metastases, while it is able to effectively eliminate secondary inoculated subcutaneously (s.c.) B16 cells from progression. This is due to the presence of an immunosuppressive TME within the primary metastases characterized by increased regulatory T cells (Tregs) and an increased T helper cells (Th) 2/1 profile. These tumour-induced immunosuppressive T cell populations are counteracted by improved adaptive immunity via active and passive immunization, resulting in effective elimination of the TME, destruction of the metastatic tumour and a reversal of Th2/1 profile in a time-sensitive manner. Thus, we here demonstrate that the TME is not irreversible and adaptive immunity is able to eradicate established solid tumour and its immunosuppressive TME. This study will help design treatments to overcome the immunosuppressive effect of the TME and improve efficacy of cancer immunotherapy. Full article

Type II transmembrane serine proteases (TTSPs) facilitate the spread and replication of viruses such as influenza and human coronaviruses, although it remains unclear whether TTSPs play a role in the progression of animal coronavirus infections, such as that by porcine epidemic diarrhea virus (PEDV). In this study, TTSPs including TMPRSS2, HAT, DESC1, and MSPL were tested for their ability to facilitate PEDV replication in Vero cells. Our results showed that TMPRSS2 and MSPL played significant roles in the stages of cell–cell fusion and virus–cell fusion, whereas HAT and DESC1 exhibited weaker effects. This activation may be involved in the interaction between TTSPs and the PEDV S protein, as the S protein extensively co-localized with TMPRSS2 and MSPL and could be cleaved by co-expression with TMPRSS2 or MSPL. Moreover, the use of Vero cells expressing TMPRSS2 and MSPL facilitated PEDV replication in the absence of exogenous trypsin. In sum, we identified two host proteases, TMPRSS2 and MSPL, which may provide insights and a novel method for enhancing viral titers, expanding virus production, and improving the adaptability of PEDV isolates in vitro. Full article