The success of missions and related costs has always been one of the concerns of researchers and due to the sensitivity and variety of missions, this issue is more important today. In the past decade, group fly is more investigated because of their influence on the quantity and quality of missions. In this research, an Unmanned Air Vehicle (UAV) system that includes several UAVs that has a parallel structure and able to perform different missions to detect, follow and attack, has been considered. The UAV success rate is be defined according to the UAV conditions, the amount of failure, and the type of failure. it is assumed that its failure rate is not constant and has multistate behavior, also It has the ability to perform different operations assigned randomly, Universal generation function, and Markov chain methods for modeling are be developed. A UAV system has been considered and, the ability and accuracy of these methods are be demonstrated.

Background & Aim: Multistate models are systems of multivariate survival data where individuals move through a series of distinct states following certain paths of possible transitions. Such models provide a relevant tool for studying observations of a continuous time process at arbitrary times. The aim of this study was to model the transitions from a healthy (hypertension free) state to an illness (hypertension) state of a hypertensive patient under treatment.Methods & Materials: In this article, the application of multistate modeling using hypertension data is demonstrated. Hospital data were obtained for a cohort of 353 patients from Jimma University Hospital, Ethiopia.Results: Three states of the Markov process are defined based on the WHO guideline of high blood pressure, state 1 (BP<140/90 mmHg), state 2 (BP≥140/90 mmHg) and state 3 (dropout). The first state is termed as a healthy state, the second an illness state and the third one is an absorbing state.Initially, the state transition intensities and state occupation probabilities are estimated with no covariate. Then, the effect of gender and family history of hypertension on the state transition intensities are evaluated separately and jointly using proportional intensities model.Conclusion: The study indicates that gender has a significant effect on the transition intensities but not family history of hypertension.

Introduction: Breast cancer is the second most common cancer and one of the most common causes of death in women. Metastasis is also a challenge for breast cancer patients, affecting their survival. This study aimed to determine the survival rate and measure the factors affecting the progression of the disease from the time of diagnosis by considering metastasis as an intermediate condition using the disease-mortality model. Methods: This retrospective study included 493 women with breast cancer referred to Imam Khomeini Clinic, Hamadan, between 2001 and 2017. First, the required information was extracted from patients’ files according to a checklist, and then the effect of background and clinical variables on the natural course of the disease was examined using a multistate model. Results: The risk of metastasis for people whose tumor size was 2-5 cm and >5 cm was higher than that for people whose tumor size was <2 cm (HR, 1. 28; [95% CI, 1. 10-1. 79]; and HR, 2. 48; [95% CI, 1. 25-3. 91], respectively). People with estrogen-, progesterone-, and epidermal growth factor– positive tumors were at higher risk for metastasis (HR, 2. 58; [95% CI, 1. 96-3. 40]; HR, 1. 69; [95% CI, 1. 7-2. 26]; and HR, 1. 73; [95% CI, 1. 29-2. 31], respectively). Conclusion: Public education on the importance of periodic examinations and early detection of breast cancer, as well as recognizing the symptoms of the disease, can help a person detect the disease early. Therefore, the patient can see a doctor before the tumor gets bigger and the disease progresses, which will help in timely treatment of the disease.