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In a life testing experiments, accelerated life tests (ALTs) model has provided a significant decrease for the cost and time. The problem of statistical inference of constant-stress ALTs based on censored data is discussed in this paper. So, we implement partially constant-stress ALTs model to test units have two parameter Kumaraswamy lifetime population under adaptive Type-II progressive censoring scheme. The population parameters as well as acceleration factor are estimated by using maximum likelihood method for point and interval estimation. Two different confidence intervals are obtained under bootstrap technique. Also, Bayesian approach under different loss functions is used to contract the point and interval estimates of the model parameters with the help of Markov chain Monte Carlo method (MCMC). For illustrative purpose a simulate data set are analyzed. Different developed results discussed in this paper are compared through Monte Carlo simulation study.

The generalized log-logistic distribution is especially useful for modelling survival data with variable hazard rate shapes because it extends the log-logistic distribution by adding an extra parameter to the classical distribution, resulting in greater flexibility in analyzing and modelling various data types. We derive the fundamental mathematical and statistical properties of the proposed distribution in this paper. Many well-known lifetime special submodels are included in the proposed distribution, including the Weibull, log-logistic, exponential, and Burr XII distributions. The maximum likelihood method was used to estimate the unknown parameters of the proposed distribution, and a Monte Carlo simulation study was run to assess the estimators’ performance. This distribution is significant because it can model both monotone and nonmonotone hazard rate functions, which are quite common in survival and reliability data analysis. Furthermore, the proposed distribution’s flexibility and usefulness are demonstrated in a real-world data set and compared to its submodels, the Weibull, log-logistic, and Burr XII distributions, as well as other three-parameter parametric survival distributions, such as the exponentiated Weibull distribution, the three-parameter log-normal distribution, the three-parameter (or the shifted) log-logistic distribution, the three-parameter gamma distribution, and an exponentiated Weibull distribution. The proposed distribution is plausible, according to the goodness-of-fit, log-likelihood, and information criterion values. Finally, for the data set, Bayesian inference and Gibb’s sampling performance are used to compute the approximate Bayes estimates as well as the highest posterior density credible intervals, and the convergence diagnostic techniques based on Markov chain Monte Carlo techniques were used.

Despite the wide medical knowledge about the direct role of many viruses in the pathogenesis of certain cancers, there is still ambiguity and hazy vision about the direct role of bacteria in cancer incidence. Understanding the role of bacteria in carcinogenesis is no longer a scientific luxury, but it has become an urgent and extremely important necessity to realize the pathogenesis of cancer caused by oncogenic bacteria as an attempt to overcome the oncogenic mechanisms exhibited by these oncogenic bacteria. This review shed the light on the indirect role of the host’s inflammatory and immunological responses in the pathogenesis of bacteria-induced cancer. Also, this review discussed the indirect role of the bacterial toxins and virulence factors in the induction of common gastrointestinal cancers, such as gallbladder cancer (GBC), colorectal cancer (CRC), and gastric cancer (GC). Finally, this review dealt with the debate about the possibility of bacterial DNA integration into the human genome and cancer incidence.

Up to day, the role of ultraviolet rays in producing cancers like melanoma has been considered extensively. However, quantum effects on the evolutions of melanoma has been discussed minorly. In this research, we propose a quantum model for the replication of DNAs within skin cells. We show that ultraviolet rays prevent of imaging information within genes of parent cells on genes of child cells and make some genetic errors in produced DNAs during replications. We compare the model with experimental data and obtain the growth rate of tumour on the skin.

DNA could emit some waves and absorb or repel drugs. Thus, DNA waves have the main role in drug’s effects on cells. Recently, some scientists have shown that the probability for survival in patients who taken nivolumab plus ipilimumab is more than in patients who obtained ipilimumab alone. We show that the reason for these events is that some drug molecules which have more charges could be absorbed more by charges on DNA within cells. We propose a physical model for calculating the attractive forces between drugs and DNA within cells. In this model, a drug is formed from charged particles and by its motions along blood vessels, some electrical currents are emerged. From other side, DNA within cells are also formed from charged particles. Charges of DNA within cells interact with charges of drugs and consequently; DNA within cells vibrate. Consequently, a cellular current is emerged which interacts with drug currents and two electrical and magnetical forces are emerged. We show that by increasing number of charged atoms within drugs and DNA, the attractive forces between cells and drugs increase. Thus, a system which is formed from two drugs like nivolumab plus ipilimumab includes more charges respect to one drug like ipilimumab alone. Consequently, this combination is more absorbed by charges on DNA within cells and is more effective. This could be generalized to any other drug that can be used in curing cancers.

The study of search plans has found considerable interest between searchers due to its interesting applications in our real life like searching for located and moving targets. This paper develops a method for detecting moving targets. We propose a novel strategy based on weight function W ( Z ) , W ( Z ) = λ H ( Z ) + ( 1 - λ ) L ( Z ) , where H ( Z ) , L ( Z ) are the total probabilities of un-detecting, and total effort respectively, is searching for moving novel coronavirus disease (COVID-19) cells among finite set of different states. The total search effort will be presented in a more flexible way, so it will be presented as a random variable with a given distribution. The objective is searching for COVID-19 which hidden in one of n cells in each fixed number of time intervals m and the detection functions are supposed to be known to the searcher or robot. We look in depth for the optimal distribution of the total effort which minimizes the probability of undetected the target over the set of possible different states. The effectiveness of this model is illustrated by presenting a numerical example.

A three-parameter discrete distribution is introduced based on a recent modification of the continuous Weibull distribution. It is one of only three discrete distributions allowing for bathtub shaped hazard rate functions. We study some of its mathematical properties, discuss estimation by the method of maximum likelihood, and describe applications to four real data sets. The new distribution is shown to outperform at least three other models including those allowing for bathtub shaped hazard rate functions.