Utilizing Bayesian inference in accelerated testing models under constant stress via ordered ranked set sampling and hybrid censoring with practical validation.

This research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios.

A stochastic scale conjugate neural network procedure for the SIRC epidemic delay differential system.

In this study, a stochastic computing structure is provided for the numerical solutions of the SIRC epidemic delay differential model, i.e. SIRC-EDDM using the dynamics of the COVID-19. The design of the scale conjugate gradient (CG) neural networks (SCGNNs) is presented for the numerical treatment of SIRC-EDDM. The mathematical model is divided into susceptible S(ρ), recovered R(ρ), infected I(ρ), and cross-immune C(ρ), while the numerical performances have been provided into three different cases. The exactitude of the SCGNNs is perceived through the comparison of the accomplished and reference outcomes (Runge-Kutta scheme) and the negligible absolute error (AE) that are performed around 10-06 to 10-08 for each case of the SIRC-EDDM. The obtained results have been presented to reduce the mean square error (MSE) using the performances of train, validation, and test data. The neuron analysis is also performed that shows the AE by taking 14 neurons provide more accurateness as compared to 4 numbers of neurons. To check the proficiency of SCGNNs, the comprehensive studies are accessible using the error histograms (EHs) investigations, state transitions (STs) values, MSE performances, regression measures, and correlation.

A novel generalized Weibull Poisson G class of continuous probabilistic distributions with some copulas, properties and applications to real-life datasets.

The current study introduces and examines copula-coupled probability distributions. It explains their mathematical features and shows how they work with real datasets. Researchers, statisticians, and practitioners can use this study's findings to build models that capture complex multivariate data interactions for informed decision-making. The versatility of compound G families of continuous probability models allows them to mimic a wide range of events. These incidents can range from system failure duration to transaction losses to annual accident rates. Due to their versatility, compound families of continuous probability distributions are advantageous. They can simulate many events, even some not well represented by other probability distributions. Additionally, these compound families are easy to use. These compound families can also show random variable interdependencies. This work focuses on the construction and analysis of the novel generalized Weibull Poisson-G family. Combining the zero-truncated-Poisson G family and the generalized Weibull G family creates the compound G family. This family's statistics are mathematically analysed. This study uses Clayton, Archimedean-Ali-Mikhail-Haq, Renyi's entropy, Farlie, Gumbel, Morgenstern, and their modified variations spanning four minor types to design new bivariate type G families. The single-parameter Lomax model is highlighted. Two practical examples demonstrate the importance of the new family.

Reliability analysis of the triple modular redundancy system under step-partially accelerated life tests using Lomax distribution.

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.

Statistical inference for a constant-stress partially accelerated life tests based on progressively hybrid censored samples from inverted Kumaraswamy distribution.

In this article, we investigate the problem of point and interval estimations under constant-stress partially accelerated life tests. The lifetime of items under use condition is assumed to follow the two-parameter inverted Kumaraswamy distribution. Based on Type-I progressively hybrid censored samples, the maximum likelihood and Bayesian methods are applied to estimate the model parameters as well as the acceleration factor. Under linear exponential, general entropy and squared error loss functions, Bayesian method outcomes are obtained. In addition, interval estimation is achieved by finding approximately confidence intervals for the parameters, as well as credible intervals. To investigate the accuracy of the obtained estimates and to compare the performance of confidence intervals, a Monte Carlo simulation is developed. Finally, a set of real data is analyzed to demonstrate the estimation procedures.

Inference for a Kavya-Manoharan Inverse Length Biased Exponential Distribution under Progressive-Stress Model Based on Progressive Type-II Censoring.

In this article, a new one parameter survival model is proposed using the Kavya-Manoharan (KM) transformation family and the inverse length biased exponential (ILBE) distribution. Statistical properties are obtained: quantiles, moments, incomplete moments and moment generating function. Different types of entropies such as Rényi entropy, Tsallis entropy, Havrda and Charvat entropy and Arimoto entropy are computed. Different measures of extropy such as extropy, cumulative residual extropy and the negative cumulative residual extropy are computed. When the lifetime of the item under use is assumed to follow the Kavya-Manoharan inverse length biased exponential (KMILBE) distribution, the progressive-stress accelerated life tests are considered. Some estimating approaches, such as the maximum likelihood, maximum product of spacing, least squares, and weighted least square estimations, are taken into account while using progressive type-II censoring. Furthermore, interval estimation is accomplished by determining the parameters' approximate confidence intervals. The performance of the estimation approaches is investigated using Monte Carlo simulation. The relevance and flexibility of the model are demonstrated using two real datasets. The distribution is very flexible, and it outperforms many known distributions such as the inverse length biased, the inverse Lindley model, the Lindley, the inverse exponential, the sine inverse exponential and the sine inverse Rayleigh model.

The multidisciplinary conservative management of a vital crown root fracture.

UNLABELLED: This case study reports on the multidisciplinary management of a maxillary canine which sustained an unusual labial crown root fracture, resulting in a large veneer-like fragment.The canine was extruded orthodontically and the fragment was re-attached using adhesive materials.This multidisciplinary solution prevented impingement on the biological width, loss of vitality and loss of tooth structure, leading to an optimal soft and hard tissue aesthetic result. Successful clinical and radiographic results after three years were observed, despite canine protected occlusion. CLINICAL RELEVANCE: Multidisciplinary management can result in an improved prognosis of the tooth and, in time, may be the most cost-effective solution for the patient. When discussing treatment options with the patient, utilization of all dental specialties should be considered and offered to the patient.

Impact of moderate and severe hypodontia and amelogenesis imperfecta on quality of life and self-esteem of adult patients.

OBJECTIVES: The objective of this study was to investigate the impact of moderate and severe hypodontia and amelogenesis imperfecta on the quality of life and self-esteem of affected adult patients. METHODS: Forty one adult patients (aged 18-45 years) with clinical and radiological diagnoses of moderate to severe hypodontia and twenty seven patients diagnosed with amelogenesis imperfecta were age and gender matched with a control group of patients attending for routine dental care. Subjects completed the Oral Health Impact Profile [OHIP-49] and Rosenberg Self Esteem Scale. A paired t-test was used to analyse data; the test alpha level was set at P ≤ 0.05. RESULTS: The results for hypodontia patients were significantly different from controls in six out of the seven OHIP-49 domains, the exception being the Handicap domain. Total scores were also significantly different between the two groups (P=0.003). Self-esteem was not significantly different between the two groups (P=0.98). For amelogenesis imperfecta patients the results were significantly different from control patients in four out of the seven domains of the OHIP-49 and also in the total scores (P=0.01). When self-esteem was investigated there was no significant differences between the two groups (P=0.92). CONCLUSIONS: Moderate to severe hypodontia and amelogenesis imperfecta have marked negative impacts on the Oral Health Related quality of life of this patient population relative to controls. However, self-esteem was not significantly affected.

Tooth agenesis in patients referred to an Irish tertiary care clinic for the developmental dental disorders.

PURPOSE: This study was carried out to determine the prevalence, severity and pattern of hypodontia in Irish patients referred to a tertiary care clinic for developmental dental disorders. MATERIALS AND METHODS: Details of 168 patients with hypodontia referred during the period 2002-2006 were entered in a database designed as a national record. Tooth charting was completed using clinical and radiographic examinations. The age of patients ranged from 7-50 years, with a median age of 20 years (Mean: 21.79; SD: 8.005). RESULTS: Hypodontia referrals constituted 65.5% of the total referrals. Females were more commonly affected than males with a ratio of 1.3:1. The number of referrals reflected the population density in this area; the majority were referrals from the public dental service. Mandibular second premolars were the most commonly missing teeth, followed by maxillary second premolars and maxillary lateral incisors; maxillary central incisors were the least affected. Symmetry of tooth agenesis between the right and left sides was an evident feature. Slightly more teeth were missing on the left side (n = 725) than on the right side (n = 706) and in the maxillary arch (n = 768) as compared to the mandibular arch (n = 663). Some 54% of patients had severe hypodontia with more than six teeth missing; 32% had moderate hypodontia, with four to six teeth missing. The most common pattern of tooth agenesis was four missing teeth. CONCLUSION: Hypodontia was a common presentation in a population referred to this tertiary care clinic. The pattern and distribution of tooth agenesis in Irish patients appears to follow the patterns reported in the literature.

Pain and anxiety following the placement of dental implants.

PURPOSE: This study investigated pain experience and anxiety following dental implant placement using questionnaires and salivary cortisol measurements. MATERIALS AND METHODS: Patients about to undergo implant placement were instructed to keep recovery diaries to assess pain experience (limitation of activities, postoperative symptoms) and to record average pain, worst pain, and interference with daily activities on a visual analog scale (VAS). To assess anxiety, patients completed the Spielberger self-evaluation questionnaire and collected salivary samples to measure cortisol levels. Saliva was collected 1 week before surgery, the day of surgery, and 3 and 6 days postoperatively. A repeated-measure analysis of variance was used to analyze pain and anxiety data. RESULTS: Eighteen patients (12 women and 6 men) who received 30 implants were recruited for the study. Following implant placement, most patients reported mild to moderate interference with daily activities and postoperative symptoms. No patient reported high levels of any symptom. Average pain experience decreased significantly with time (F = 6.17; P < .001), from a VAS score of 24/100 on day 1 to 12 on day 3 and 9 on day 6. Worst pain (F = 7.84; P < .001) and limitation of daily activities (F= 6.26; P < .001) were also highest on the first postoperative day; they also decreased to about half the maximum level by the second or third day. State anxiety, as evaluated by the Spielberger self-evaluation scale, was highest on the day of surgery. The salivary cortisol level did not validate this, as it did not differ with the time of collection (F = 2.22; P = .075). CONCLUSIONS: Patient self-assessment indicates that implant placement is a mild to moderately painful and anxiety-provoking procedure. Some limitation of daily activities and symptoms are expected to occur, particularly during the first 3 postoperative days.