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This study considers one of the partially accelerated life testing models, mainly a constant-stress approach with unified hybrid censored data. The data represents the lifetime of units following the exponentiated gamma distribution (EGD). The estimation techniques for the parameter of EGD and the acceleration factor are discussed, along with the maximum likelihood estimation technique, the Bayesian and E-Bayesian estimation. Utilizing the squared error loss (SEL) and LINEX loss functions, the E-Bayesian and Bayesian estimates are generated. For deriving the estimates of Bayesian and E-Bayesian approaches, we use the MCMC method. Real-world data is also provided for illustration. Furthermore, unified hybrid censoring schemes (HCSs) are examined to determine their effectiveness in the light of the results. In the end, we compare among the proposed methods during their findings. The study's findings demonstrate how well the suggested techniques work when utilizing unified HCS. The study also makes clear how important it is to adopt the CSPALT model, as it yields more failed things in a shorter amount of time than items with typical use situations. In conclusion, it is suggested that this research be used to tasks like industrial product reliability testing.

In applied sectors, data modeling/analysis is very important for decision-making and future predictions. Data analysis in applied sectors mainly relies on probability distributions. Data arising from numerous sectors such as engineering-related fields have complex structures. For such kinds of data having complex structures, the implementation of classical distributions is not a suitable choice. Therefore, researchers often need to look for more flexible models that might have the capability of capturing a high degree of kurtosis and increasing the fitting power of the classical models. Taking motivation from the above theory, to achieve these goals, we study a new probabilistic model, which we named a new beta power flexible Weibull (NBPF-Weibull) distribution. We derive some of the main distributional properties of the NBPF-Weibull model. The estimators for the parameters of the NBPF-Weibull distribution are derived. The performances of these estimators are judged by incorporating a simulation study for different selected values of the parameters. Three data sets are used to demonstrate the applicability of the NBPF-Weibull model. The first data set is observed from sports. It represents the re-injury rate of various football players. While the other two data sets are observed from the reliability zone. By adopting certain diagnostic criteria, it is proven that the NBPF-Weibull model repeatedly surpasses well-known classical and modified models.

We aim in this paper to propose a novel class of distributions that was created by merging the Topp-Leone distribution and the Generated families of Kumaraswamy and Marshall-Olkin. Its cumulative distribution function characterizes it and includes rational and polynomial functions. In particular, the following desirable properties of the new family are presented: Shannon entropy, order statistics, the quantile power series, and several associated measures and functions. Then, using a specific family member identified before, we create a parametric statistical model with the basic distribution being the inverse exponential distribution. Finally, a thorough investigation has been made to implement this new distribution with three data sets: the glass fibers data set, the glass Alumina data set and the hailing times data set. In comparison to six prominent competitors, the new model performs favorably on all statistical tests and criteria that were examined.

The Lomax model, also known as Pareto Type-II, has broad feasibility, especially in financing. This work introduces a new generalization of the Lomax model called the arc-sine exponentiation Lomax, which helps consider economic phenomena. The arc-sine exponentiation Lomax distribution captures a variety of shapes of density and hazard functions. The estimators of the proposed model’s parameters are derived using the maximum likelihood method. In a simulation study, the accuracy and efficacy of estimators are evaluated by computing their mean square errors and biases.Furthermore, a bivariate extension of the arc-sine exponentiation Lomax model is also introduced. The bivariate extension is introduced using Farlie–Gumble–Morgenstern copula approach. The new bivariate model is called Farlie–Gumble–Morgenstern arc-sine exponentiation Lomax distribution. Finally, a data set of thirty-two observations representing the export of goods demonstrates the arc-sine exponentiation Lomax model. The best-fitting results of the arc-sine exponentiatial Lomax are compared with some prominent extensions of the Lomax distribution.

In this article, we suggest an enhanced estimator for the estimation of finite population variance using twofold auxiliary variable under stratified random sampling. The numerical expressions for the bias and MSE are determined up to the first order of approximation. In order to effectively validate the theoretical findings, three actual data sets are included. Additionally, the application of the suggested estimators is demonstrated using a simulation study. Results of an empirical comparison among the suggested and existing estimators were investigated. To determine how good the suggested estimator, in comparison to the preliminary estimators, the MSE criterion is used. The suggested estimator has a smaller MSE and better PRE than existing estimators, according to numerical results utilizing actual data sets and a simulation analysis.

This research presents a novel approach for cervical cancer detection and segmentation using tissue images with multiple cells. The study employs a novel deep learning architecture based on Mask Region-Based Convolutional Neural Network (RCNN) and statistical analysis. This new architecture enables us to achieve a high percentage of detection and pix-to-pix area segmentation. A mean Average Precision (mAP) higher than 60% for 3-class and 5-class was achieved. In addition, higher F1-scores of 70% for 3-class and 5-class were obtained. This investigation is a collaborative work, where a medical consultant collected the samples from the Papanicolaou (Pap) Smear examination and labeled the cells presented to the liquid-based cytology (LBC). Furthermore, the online available benchmark data set, SIPaKMeD, was also utilized. Additionally, sample images from the Mendeley data set were also labeled by the trained medical consultant for comparison. The proposed scheme automatically generates a full report for a medical consultant to identify the location of the malicious cells in the given images and expedite the diagnosis and treatment process.

When measuring the research variable is complicated, expensive, or problematic, median ranked set sampling (MRSS) is often utilized since it is straightforward to rank the components using a low-cost sorting criterion. Using this sampling scheme, many authors considered the problem of population mean estimation with a single auxiliary variable in order to obtain more precised estimators than the traditional ratio type regression estimators. In this article, we extend their ideas based on regression approach using two auxiliary variables and introduce a new regression-type estimator along with its theoretical expression of minimum mean square error (MSE). The suggested estimator's applicability is demonstrated using both simulated and real-world data sets.

In this study, the parameters of the inverted exponentiated Lomax distribution via generalized order statistics are assessed using Bayesian and maximum likelihood approaches. The maximum likelihood estimators along with approximate confidence intervals are calculated. Under the squared error loss function, the Bayesian estimator, percentile bootstrap, and bootstrap-t credible periods are produced. Furthermore, the proposed estimators are dedicated to schemes such as type-II censored ordinary order statistics joint density function. A numerical simulation is used to assess the behavior and sensitivity of the estimates for various sample sizes. From the posterior distributions, the Metropolis–Hastings technique is used to generate Markov chain Monte Carlo samples. We utilize this technique to examine a current dataset of interest: daily cases of COVID-19 instances detected in Saudi Arabia from May 31 to October 28, 2020 (inclusive). In the future, the proposed methodology could be useful for analyzing data on COVID-19 instances in other countries for comparative studies.

This article discusses the problem of estimation with step stress partially accelerated life tests using Type-II progressively censored samples. The lifetime of items under use condition follows the two-parameters inverted Kumaraswamy distribution. The maximum likelihood estimates for the unknown parameters are computed numerically. Using the property of asymptotic distributions for maximum likelihood estimation, we constructed asymptotic interval estimates. The Bayes procedure is used to calculate estimates of the unknown parameters from symmetrical and asymmetric loss functions. The Bayes estimates cannot be obtained explicitly, therefor the Lindley's approximation and the Markov chain Monte Carlo technique are used to obtaining the Bayes estimates. Furthermore, the highest posterior density credible intervals for the unknown parameters are calculated. An example is presented to illustrate the methods of inference. Finally, a numerical example of March precipitation (in inches) in Minneapolis failure times in the real world is provided to illustrate how the approaches will perform in practice.

This work uses the inverse-power transformation to create the inverse power Ramos–Louzada distribution (IPRLD), a novel two-parameter version of the Ramos–Louzada distribution. The failure rate of the new distribution can be represented by a reverse bathtub shape, a rising shape, or a decreasing shape, making it appropriate for a range of real data. Asymmetrical and unimodal densities can be produced via the IPRLD. Its mathematical characteristics are computed in some cases. The novel proposed model’s structural characteristics are derived. To estimate the model parameters, several estimating strategies are explored, including ten classical methods. Simulation results with their partial and total ranks are used to evaluate the ranking and behavior of various approaches. Finally, two real-world datasets are used to experimentally show the suggested distribution’s adaptability. The analysis of the data reveals that the introduced distribution offers a better fit than some significant rival distributions, including the inverse Ramos–Louzada, inverse power Burr Hatke, inverse Nakagami-M, inverse log-logistic, inverse weighted Lindley, inverse Lindley, and Ramos–Louzada.

The quadratic rank transmutation map is used in this article to suggest a novel extension of the power inverted Topp–Leone distribution. The newly generated distribution is known as the transmuted power inverted Topp–Leone (TPITL) distribution. The power inverted Topp–Leone and the inverted Topp–Leone are included in the recommended distribution as specific models. Aspects of the offered model, including the quantile function, moments and incomplete moments, stochastic ordering, and various uncertainty measures, are all discussed. Plans for acceptance sampling are created for the TPITL model with the assumption that the life test will end at a specific time. The median lifetime of the TPITL distribution with the chosen variables is the truncation time. The smallest sample size is required to obtain the stated life test under a certain consumer's risk. Five conventional estimation techniques, including maximum likelihood, least squares, weighted least squares, maximum product of spacing, and Cramer-von Mises, are used to assess the characteristics of TPITL distribution. A rigorous Monte Carlo simulation study is used to evaluate the effectiveness of these estimators. To determine how well the most recent model handled data modeling, we tested it on a range of datasets. The simulation results demonstrated that, in most cases, the maximum likelihood estimates had the smallest mean squared errors among all other estimates. In some cases, the Cramer-von Mises estimates performed better than others. Finally, we observed that precision measures decrease for all estimation techniques when the sample size increases, indicating that all estimation approaches are consistent. Through two real data analyses, the suggested model's validity and adaptability are contrasted with those of other models, including the power inverted Topp–Leone, log-normal, Weibull, generalized exponential, generalized inverse exponential, inverse Weibull, inverse gamma, and extended inverse exponential distributions.

In real-life experiments, collecting complete data is time-, finance-, and resources-consuming as stated by statisticians and analysts. Their goal was to compromise between the total time of testing, the number of units under scrutiny, and the expenditures paid through a censoring scheme. Comparing failure-censored schemes (Type-Ⅱ and Progressive Type-Ⅱ) to Time-censored schemes (Type-Ⅰ), it's worth noting that the former is time-consuming and is no more suitable to be applied in real-life situations. This is the reason why the Type-Ⅰ adaptive progressive hybrid censoring scheme has exceeded other failure-censored types; Time-censored types enable analysts to accomplish their trials and experiments in a shorter time and with higher efficiency. In this paper, the parameters of the inverse Weibull distribution are estimated under the Type-Ⅰ adaptive progressive hybrid censoring scheme (Type-Ⅰ APHCS) based on competing risks data. The model parameters are estimated using maximum likelihood estimation and Bayesian estimation methods. Further, we examine the asymptotic confidence intervals and bootstrap confidence intervals for the unknown model parameters. Monte Carlo simulations are carried out to compare the performance of the suggested estimation methods under Type-Ⅰ APHCS. Moreover, Markov Chain Monte Carlo by applying Metropolis-Hasting algorithm under the square error of loss function is used to compute Bayes estimates and related to the highest posterior density. Finally, two data sets are studied to illustrate the introduced methods of inference. Based on our results, we can conclude that the Bayesian estimation outperforms the maximum likelihood estimation for estimating the inverse Weibull parameters under Type-Ⅰ APHCS.

In this article, we extensively study a family of distributions using the trigonometric function. We add an extra parameter to the sine transformation family and name it the alpha-sine-G family of distributions. Some important functional forms and properties of the family are provided in a general form. A specific sub-model alpha-sine Weibull of this family is also introduced using the Weibull distribution as a parent distribution and studied deeply. The statistical properties of this new distribution are investigated and intended parameters are estimated using the maximum likelihood, maximum product of spacings, least square, weighted least square, and minimum distance methods. For further justification of these estimates, a simulation experiment is carried out. Two real data sets are analyzed to show the suggested model’s application. The suggested model performed well compares to some existing models considered in the study.

Malakoplakia is a rare chronic granulomatous disease that may affect many organs, including the lung, brain, adrenal glands, pancreas, bone, and the genitourinary tract. The gastrointestinal tract is the most common site of involvement outside of the urinary tract. Herein, we present a case of a 65-year-old male who presented with abdominal pain and unintentional weight loss. Physical examination findings were unremarkable, but colonoscopy revealed an isolated large, flat, soft, and pale lesion in the transverse colon. Histopathological examination of the lesion showed expansion of the lamina propria due to numerous lymphocytes, plasma cells, neutrophils, and polygonal cells. Von Kossa stain showed small intracytoplasmic basophilic granular inclusions, and these histiocyte cells showed numerous Michaelis-Gutmann bodies, findings considered as diagnostic features of colonic malakoplakia. This is a rare case of isolated malakoplakia of the transverse colon diagnosed on endoscopic specimens. The majority of reported cases have shown an association between systemic diseases and colorectal adenocarcinoma. In addition, most reported cases of colonic involvement have been in the rectum, sigmoid, and right colon.

OBJETIVO: Realizar uma comparação crítica entre a Relação Nacional de Medicamentos Essenciais (Rename, 2012) e a Lista de Medicamentos Essenciais para Crianças (LMEC, 2011) da Organização Mundial de Saúde (OMS), com relação às diferenças entre os medicamentos e as formulações listadas para crianças. MÉTODOS: Os medicamentos da LMEC foram classificados em quatro grupos: 1) não constam na Rename; 2) constam na Rename, porém sem qualquer formulação adequada para crianças; 3) listados na Rename apenas com algumas formulações; 4) constam na Rename em todas as formulações. As formulações que faltam foram analisadas por grupos terapêuticos. As alternativas presentes na Rename foram pesquisadas. RESULTADOS: Dos 261 medicamentos de interesse listados na LMEC, 30,3% não estão presentes na Rename, 11,1% estão na Rename, mas sem qualquer formulação pediátrica, e 32,3% estão presentes em algumas, mas não todas as formulações listadas na LMEC. Considerando todos os itens de formulações listados na LMEC (n = 577), 349 não constam na Rename, desses, 19,6% devido à intensidade de dosagem, e 18,5% devido à forma farmacêutica. Faltam formulações úteis específicas para cuidado neonatal, trato respiratório e sistema nervoso central, anti-infecciosos, entre outros grupos. CONCLUSÃO: A ausência de formulações adequadas à idade de medicamentos essenciais para crianças no Brasil inclui importantes grupos terapêuticos e medicamentos indispensáveis para quadros clínicos graves. Alguns desses produtos são encontrados no mercado farmacêutico brasileiro, porém não existem em unidades públicas; outros poderiam ser produzidos por laboratórios nacionais com interesse comercial ou estimulados por uma política governamental específica, como é feito em outros países.OBJECTIVE: To perform a critical comparison between the Brazilian national essential medicines list (Rename, 2012) with the list of essential medicines for children (LEMC, 2011) of the World Health Organization (WHO), regarding the differences among drugs and formulations listed for children. METHODS: The LEMC drugs were classified into four categories: 1) absent in Rename; 2) included in Rename but without any formulation suitable for children; 3) listed in Rename only in some formulations; 4) present in Rename in all formulations. The missing formulations were analyzed by therapeutic group. Alternatives present in Rename were searched. RESULTS: From the 261 drugs of interest on the LEMC, 30.3% are absent from Rename, 11.1% are in Rename but without any pediatric formulation, and 32.2% are present in some but not all formulations listed in LEMC. Considering all formulations items listed in the LEMC (n = 577), 349 are missing from Rename, of these 19.6% due to their strength, and 18.5% due to the the dosage form. Useful formulations specific for neonatal care, respiratory tract, central nervous system, and anti-infectives, among other groups, are missing. CONCLUSIONS: The lack of age-appropriate formulations of essential medicines for children in Brazil includes important therapeutic groups and indispensable drugs for severe clinical conditions. Some of these products exist in the Brazilian pharmaceutical market, but not in public facilities; others could be produced by national laboratories with commercial interest or stimulated by a specific governmental policy, as in other countries.

Objective: To perform a critical comparison between the Brazilian national essential medicines list (Rename, 2012) with the list of essential medicines for children (LEMC, 2011) of the World Health Organization (WHO), regarding the differences among drugs and formulations listed for children. Methods: The LEMC drugs were classified into four categories: 1) absent in Rename; 2) included in Rename but without any formulation suitable for children; 3) listed in Rename only in some formulations; 4) present in Rename in all formulations. The missing formulations were analyzed by therapeutic group. Alternatives present in Rename were searched. Results: From the 261 drugs of interest on the LEMC, 30.3% are absent from Rename, 11.1% are in Rename but without any pediatric formulation, and 32.2% are present in some but not all formulations listed in LEMC. Considering all formulations items listed in the LEMC (n = 577), 349 are missing from Rename, of these 19.6% due to their strength, and 18.5% due to the the dosage form. Useful formulations specific for neonatal care, respiratory tract, central nervous system, and anti-infectives, among other groups, are missing. Conclusion: The lack of age-appropriate formulations of essential medicines for children in Brazil includes important therapeutic groups and indispensable drugs for severe clinical conditions. Some of these products exist in the Brazilian pharmaceutical market, but not in public facilities; others could be produced by national laboratories with commercial interest or stimulated by a specific governmental policy, as in other countries.

In this paper, the problem of estimating the shear force affecting the tip of the cantilever in a Transverse Dynamic Force Microscope (TDFM) using a real-time implementable sliding mode observer is addressed. The behaviour of a vertically oriented oscillated cantilever, in close proximity to a specimen surface, facilitates the imaging of the specimen at nano-metre scale. Distance changes between the cantilever tip and the specimen can be inferred from the oscillation amplitudes, but also from the shear force acting at the tip. Thus, the problem of accurately estimating the shear force is of significance when specimen images and mechanical properties need to be obtained at submolecular precision. A low order dynamic model of the cantilever is derived using the method of lines, for the purpose of estimating the shear force. Based on this model, an estimator using sliding mode techniques is presented to reconstruct the unknown shear force, from only tip position measurements and knowledge of the excitation signal applied to the top of the cantilever. Comparisons to methods assuming a quasi-static harmonic balance are made.

Lapachol is a naphthoquinone found in several species of the Bignoniaceae family possessing mainly anticancer activity. The present work consists of the development and validation of analytical methodology for lapachol and its preparations. The results here obtained show that lapachol has a low quantification limit, that the analytical methodology is accurate, reproducible, robust and linear over the concentration range 0.5-100 µg/mL of lapachol.

This paper introduces the generalized exponential- U family of distributions as a novel methodological approach to enhance the distributional flexibility of existing classical and modified distributions. The new family is derived by combining the T- X family method with the exponential model. The paper presents the generalized exponential-Weibull model, an updated version of the Weibull model. Estimators and heavy-tailed characteristics of the proposed method are derived. The new model is applied to three healthcare data sets, including COVID-19 patient survival times and mortality rate data set from Mexico and Holland. The proposed model outperforms other models in terms of analyzing healthcare data sets by evaluating the best model selection measures. The findings suggest that the proposed model holds promise for broader utilization in the area of predicting and modeling healthcare phenomena. This paper introduces the generalized exponential- family of distributions as a novel methodological approach to enhance the distributional flexibility of existing classical and modified distributions. The new family is derived by combining the T- family method with the exponential model. The paper presents the generalized exponential-Weibull model, an updated version of the Weibull model. Estimators and heavy-tailed characteristics of the proposed method are derived. The new model is applied to three healthcare data sets, including COVID-19 patient survival times and mortality rate data set from Mexico and Holland. The proposed model outperforms other models in terms of analyzing healthcare data sets by evaluating the best model selection measures. The findings suggest that the proposed model holds promise for broader utilization in the area of predicting and modeling healthcare phenomena. [ABSTRACT FROM AUTHOR]

In this article, we extensively study a family of distributions using the trigonometric function. We add an extra parameter to the sine transformation family and name it the alpha-sine-G family of distributions. Some important functional forms and properties of the family are provided in a general form. A specific sub-model alpha-sine Weibull of this family is also introduced using the Weibull distribution as a parent distribution and studied deeply. The statistical properties of this new distribution are investigated and intended parameters are estimated using the maximum likelihood, maximum product of spacings, least square, weighted least square, and minimum distance methods. For further justification of these estimates, a simulation experiment is carried out. Two real data sets are analyzed to show the suggested model's application. The suggested model performed well compares to some existing models considered in the study. [ABSTRACT FROM AUTHOR]

The aim of our study was to explore the antioxidant potential of Moroccan saffron stigmas (SS) and examine their performance as a natural antioxidant in enhancing the stability and quality properties of edible sunflower oil (SO), with a focus on understanding the critical importance of edible oil stability for predicting quality deterioration during storage. Bearing this in mind, our research was designed to compare the efficacy of three concentrations of dried SS (at 0.2%, 0.3%, and 0.6%) against tocobiol (a synthetic antioxidant) as a positive control (at 0.3% concentration). This comparison was conducted under three distinct storage conditions namely accelerated ageing (60 °C), exposure to light at ambient temperature, and darkness, to evaluate their impact on preventing severe oxidation and extending oil shelf-life. Oxidation state evolution was evaluated through peroxide value (PV), free fatty acid (FFA), anisidine value (p-AV), K270 value (conjugated trienes), total oxidation index (TOTOX), iodine value (IV), and fatty acid composition (FA). Our results revealed notable differences in stability tracking parameters. Specifically, these parameters were higher in samples stored under accelerated conditions, followed by the samples stored in ambient light, while those stored in darkness showed the highest stability among the three storage conditions. Supplementation of sunflower oil with SS and tocobiol significantly enhanced its oxidation stability. Notably, SS exhibited exceptional effectiveness in stabilizing sunflower oil regardless of SS dose, with the highest efficacy observed at 0.6%. This was evidenced by the slower rate of oxidation parameters under various storage conditions, highlighting a superior antioxidant activity compared to both the non-enriched oil and tocobiol-enriched oil. Furthermore, saffron stigmas, used as a natural supplement, contributed to the preservation of polyunsaturated fatty acids, indicating its potential as a robust source of natural antioxidants in sunflower oil. These attributes position SS as a promising alternative to synthetic antioxidants, offering opportunities to enhance the nutritional quality and extend edible oil shelf-life.