abdol hamid rezaei roknabadi

Sometimes in order to estimate population parameters such as mean and total values, we extract a random sample by cluster sampling method, and after completing sampling, we are interested in using the same sample to estimate the desired parameters in a subset of the population, which is said subpopulation. In this paper, we try to estimate subpopulation parameters in different cases when one-stage cluster sampling design is used.

The subject of this article is about the lifetime of solar devices consisting of number (n) of tubes. The solar energy devices, when they reach an aging stage, the tubes start to fail, the maintenance costs increase, and the production of the device decreases.The costs of solar energy device maintenance companies increase as the device gets old. Therefore, maintenance companies are looking for the age to replace the device with a new device. In this article, we searched for the best age to replace device (in aging stage) with a new device.By relying on strategy of balance the costs of tubes failure in unexpected time, we have determined the optimum time for preventive replacement of solar energy device. Then we studied the factors that influence the optimum time for preventive replacement of solar energy device.

In this article- we study devices that derive energy from natural process (sun, wind, winter, soil, etc.) and that are replenished constantly such as fans generating electric power and solar energy devices. However, all devices are exposed to damage over time resulting in the accumulation of the damage caused by climatic fluctuations (Every geographical area is characterized by bad weather characteristics that leave damage to the device; like wind, rain and humidity) that lead to the failure of the device. These devices receive energy directly from nature in order to supply it to other systems (mechanical, electrical, etc.). A failure of the device reduces electrical-mechanical production. The companies manufacture renewable energy devices and export them to other countries in various geographical locations. The devices are used to provide electrical current in these countries. These companies seek to develop long-term protection plans against the device failure. A failed device becomes ineligible even for recycling in these companies. Therefore, the cost of the device failure and forced replacement becomes too expensive for these companies. Because of this, companies tend to find the optimal time to replace the device shortly before failure to reduce the cost of failure. In this experiment we study a device that is subject to shocks and calculate the optimal time for preventive replacement of a said device. As an example a solar energy device exposed to shocks resulting from climate fluctuations. We place this device in three different geographical locations (desert, tropical, and temperate), and calculate the optimal time for preventive replacement of the device. Finally, the results from these three locations are compared.

The prevalence of HIV is increasing in Iran, so obtaining an estimate of the survival of HIV-infected persons can be helpful to prevent and control this infection. This research aimed to use the Bayesian joint model by which identifies factors associated with the survival and determine the relationship between the trend of CD4+ T cell counts and survival time in HIV-infected persons. In this retrospective cohort study, we collected HIV/AIDS surveillance data from Mashhad’s Counseling Center of Behavioral Diseases in the province of Khorasan Razavi, Northeast of Iran, during 1994 - 2014. Data collection included variables CD4+ T cells count, survival time, and other related factors. We used the Bayesian joint model to estimate the survival time and identify the factors associated with survival time in HIV-infected persons. The study included 260 individuals, of whom 212 (81.54%) were male. The survival sub-model of the joint model identified gender (95% credible interval (CI): 0.486, 3.197) and antiretroviral treatment (95% CI: -1.935, -0.641) as the variables associated with the patients’ survival. The longitudinal sub-model, which determined the variables associated with the number of CD4+ T-cells included time (95% CI: -0.934, -0.554), age (95% CI: -0.152, -0.011), and antiretroviral treatment (95% CI: -6.193, -3.505). Using CD4+ T cells as a covariate in the Bayesian joint model, the survival time for HIV-infected persons was estimated more precisely than separate model and it can be inferred that at the beginning of antiretroviral treatment, especially in men and controls, the CD4+ T cell counts can increase the survival time of HIV-infected persons.

In this paper، first the Bhattacharray and Kshirsagar bounds are introduced and then the multiparameter Bhattacharyya bound is presented in simpler and understandable form. Furthermore، the multiparameter Kshirsagar lower bound، which has not been studied yet، is obtained. Finally، by presenting some example of Log-normal distribution، the bounds are computed and compared.