Brane Cosmology With Generalized Chaplygin Gas in The Bulk

We find exact solution of the Einstein equations in the context of the brane world scenario. We have supposed a {generalized chaplygin gas} equation of state for bulk. This study display a constant energy density and pressure for bulk in late time. It is shown that our assumptions impose a specific equation of state on brane. {In this work, we have obtained a decelerate universe in early time and late time.} In the end, it is shown that under some assumption we have equation of state of cosmological constant for bulk.Comment: 11 page

Small ensemble of kriging models for optimization

The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP. The important factor that controls the efficiency of EGO is the GP covariance function (or kernel) which should be chosen according to the objective function. Traditionally, a pa-rameterized family of covariance functions is considered whose parameters are learned through statistical procedures such as maximum likelihood or cross-validation. However, it may be questioned whether statistical procedures for learning covariance functions are the most efficient for optimization as they target a global agreement between the GP and the observations which is not the ultimate goal of optimization. Furthermore, statistical learning procedures are computationally expensive. The main alternative to the statistical learning of the GP is self-adaptation, where the algorithm tunes the kernel parameters based on their contribution to objective function improvement. After questioning the possibility of self-adaptation for kriging based optimizers, this paper proposes a novel approach for tuning the length-scale of the GP in EGO: At each iteration, a small ensemble of kriging models structured by their length-scales is created. All of the models contribute to an iterate in an EGO-like fashion. Then, the set of models is densified around the model whose length-scale yielded the best iterate and further points are produced. Numerical experiments are provided which motivate the use of many length-scales. The tested implementation does not perform better than the classical EGO algorithm in a sequential context but show the potential of the approach for parallel implementations

Assessing of Preparedness for Disasters and Crisis in Centers of Trauma and Accidents of Kermanshah University of Medical Sciences in 2016

Background and aims: Natural and technologic disasters and accidents have great influence on people's lifestyle and their health. Main object of hospitals is providing fast and timely health care to reduce mortality and complications by the disaster. The aim of this study is to evaluate preparedness crisis and disasters in centers of trauma of Kermanshah University of Medical Sciences. Methods: The present descriptive, cross-sectional study was conducted in three hospitals (A,B,C) of Kermanshah university of medical sciences, Iran, 2016. Data were collected using a self-administered checklist and questioner through observation and interview. The checklist included 220 yes/no questions in 10 domains of emergency (30 questions), admission (24 questions), evacuation and transfer (30 questions), traffic (15 questions), communication (16 questions), security (17 questions), education (17 questions), support (28 questions), human workforce (21 questions), and leadership and management (22 items). Scores 0 and 1 were given to “No” and “Yes” choices, respectively. Data were analyzed using SPSS and descriptive statistics. Results: Overall, the relative mean of disaster preparedness in the study hospitals A, B and C was 99.1, 43.4and 84.7, respectively. Generally, the average readiness score for all hospitals was 75. The most and lowest preparedness was related to the management and traffic domains. Conclusion: According to the results, preparedness of hospitals was in the suitable level. Officials of medical centers have the necessary programs and educations in all areas of disaster preparedness for quick response and timely in hospitals