Cost-Effective Scheduling and Load Balancing Algorithms in Cloud Computing Using Learning Automata

Cloud computing is a distributed computing model in which access is based on demand. A cloud computing environment includes a wide variety of resource suppliers and consumers. Hence, efficient and effective methods for task scheduling and load balancing are required. This paper presents a new approach to task scheduling and load balancing in the cloud computing environment with an emphasis on the cost-efficiency of task execution through resources. The proposed algorithms are based on the fair distribution of jobs between machines, which will prevent the unconventional increase in the price of a machine and the unemployment of other machines. The two parameters Total Cost and Final Cost are designed to achieve the mentioned goal. Applying these two parameters will create a fair basis for job scheduling and load balancing. To implement the proposed approach, learning automata are used as an effective and efficient technique in reinforcement learning. Finally, to show the effectiveness of the proposed algorithms we conducted simulations using CloudSim toolkit and compared proposed algorithms with other existing algorithms like BCO, PES, CJS, PPO and MCT. The proposed algorithms can balance the Final Cost and Total Cost of machines. Also, the proposed algorithms outperform best existing algorithms in terms of efficiency and imbalance degree

Climate Change Projection and Time-varying Multi-dimensional Risk Analysis

In recent decades, population growth and global warming consequent to greenhouse gas emissions because of human activities, has changed the atmospheric composition leading to intensifying extreme climate phenomena and overall increase of extreme events. These extreme events have caused human suffering and devastating effects in recent record-breaking warming years. To mitigate adverse consequences arising from global warming, the best strategy is to project the future probabilistic behavior of extreme climate phenomena under changing environment. The first contribution of this research is to improve the predictive power of regression-based statistical downscaling processes to accurately project the future behavior of extreme climate phenomena. First, a supervised dimensionality reduction algorithm is proposed for the statistical downscaling to derive a low-dimensional manifold representing climate change signals encoding of high-dimensional atmospheric variables. Such an algorithm is novel in climate change studies as past literature has focused on deriving low-dimensional principal components from large-scale atmospheric projectors without taking into account the target hydro-climate variables. The new algorithm called Supervised Principal Component analysis (Supervised PCA) outperforms all of the existing state-of-the-art dimensionality reduction algorithms. The model improves the performance of the statistical downscaling modelling through deriving subspaces that have maximum dependency with the target hydro-climate variables. A kernel version of Supervised PCA is also introduced to reduce nonlinear dimensionality and capture all of the nonlinear and complex variabilities between hydro-climate response variable and atmospheric projectors. To address the biases arising from difference between observed and simulated large-scale atmospheric projectors, and to represent anomalies of low frequency variability of teleconnections in General Circulation Models (GCMs), a Multivariate Recursive Nesting Bias Correction (MRNBC) is proposed to the regression-based statistical downscaling. The proposed method is able to use multiple variables in multiple locations to simultaneously correct temporal and spatial biases in cross dependent multi-projectors. To reduce another source of uncertainty arising from complexity and nonlinearity in quantitative empirical relationships in the statistical downscaling, the results demonstrate the superiority of a Bayesian machine-learning algorithm. The predictive power of the statistical downscaling is therefore improved through addressing the aforementioned sources of uncertainty. This results in improvement of the projection of the global warming impacts on the probabilistic behavior of hydro-climate variables using future multi-model ensemble GCMs under forcing climate change scenarios. The results of two Design-of-Experiments also reveal that the proposed comprehensive statistical downscaling is credible and adjustable to the changes under non-stationary conditions arising from climate change. Under the impact of climate change arising from anthropogenic global warming, it is demonstrated that the nature and the risk of extreme climate phenomena are changed over time. It is also well known that the extreme climate processes are multi-dimensional by their very nature characterized by multi-dimensions that are highly dependent. Accordingly, to strength the reliability of infrastructure designs and the management of water systems in the changing climate, it is of crucial importance to update the risk concept to a new adaptive multi-dimensional time-varying one to integrate anomalies of dynamic anthropogenically forced environments. The main contribution of this research is to develop a new generation of multivariate time-varying risk concept for an adaptive design framework in non-stationary conditions arising from climate change. This research develops a Bayesian, dynamic conditional copula model describing time-varying dependence structure between mixed continuous and discrete marginals of extreme multi-dimensional climate phenomena. The framework is able to integrate any anomalies in extreme multi-dimensional events in non-stationary conditions arising from climate change. It generates iterative samples using a Markov Chain Monte Carlo (MCMC) method from the full conditional marginals and joint distribution in a fully likelihood-based Bayesian inference. The framework also introduces a fully Bayesian, time-varying Joint Return Period (JRP) concept to quantify the extent of changes in the nature and the risk of extreme multi-dimensional events over time under the impact of climate change. The proposed generalized time-dependent risk framework can be applied to all stochastic multi-dimensional climate systems that are under the influence of changing environments

The survey of patient safety culture among nurses in hospitals affiliated to Zahedan university of medical sciences in 2014

Introduction: Surveying and diagnosing patient safety culture is a key step to improve the health and patient safety culture, which generally is provided by surveying of providers and measuring globally. So the present study aimed to survey and evaluate patient safety culture among nurses in hospitals affiliated to Zahedan university of medical science.Methods: This descriptive-analytical, cross-sectional study was conducted in 2014. In order to collect data, a questionnaire was developed to survey patient safety culture on 400 nurses in hospitals related to Zahedan university of medical sciences. The results were analyzed using SPSS software. The required tests such as T-test, ANOVA, and Spearman correlation coefficient were used.Results: The results of the present study showed that the score of patient safety culture for each aspect of it was low. Findings also showed that items such as general perception to patient safety, management support, and organizational learning and permanent promotion in Khatam ol Anbia, Ali Ibn Abi Taleb, and Bu Ali hospitals had the highest scores, while hospital patient transfer and exchange of information, manager’s actions, expectation to promote patient safety, and related issues to staffs had the lowest scoresConclusion: Patient safety care is necessary for providing an appropriate and efficient healthcare. So the organizations which provide healthcare services must establish a comprehensive and regular system based on the processes of patient safety promotion to decrease errors. They should also be responsive to injured people through establishing a patient safety culture and maintaining appropriate organizational mechanisms

A new time-varying concept of risk in a changing climate

In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.The authors gratefully acknowledge the financial support of the Natural Sciences and Engineering Research Council (NSERC) of Canada

Effects of information and communication technology improvement on revisit intention during Covid-19 Edit Download

Purpose- The present study aims to investigate the interrelationship between information and communication technology (ICT) improvement, destination brand image, destination satisfaction, and destination personality, and how ICT improvement influences tourists' intention to return during the pandemic COVID -19. Design, methodology, approach- The statistical population consisted of Iranian tourists who had stayed in hotels in Iran during CPVID-19. A questionnaire was developed and distributed, and 310 usable questionnaires were received. To test the hypotheses, confirmatory factor analysis was performed using Smart PLS3. Findings- Our results showed that ICT improvement had a significant, positive impact on tourists' revisit intentions and destination brand image during the pandemic COVID -19. Destination brand image also had a significant influence on revisit intention, destination satisfaction, and destination personality. In addition, destination satisfaction and destination personality were significantly related to revisit intention during the pandemic. Originality of the research- This study pioneered the evaluation of ICT in the tourism industry, focusing on the hospitality industry during the pandemic COVID -19. It also examined the direct impact of ICT improvements on revisit intentions during the pandemic. In addition, this study provides evidence for managers to more effectively leverage ICT potential to improve destination brand image and encourage customers to revisit during a pandemic

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments.We thank the associate editor and three anonymous reviewers whose suggestions helped improve the paper. We acknowledge the CMIP5 climate coupled modelling groups, for producing and making their model outputs available, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison (PCMDI), which provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The CMIP5 model outputs used in the present study are available from http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. We also thank the Iran Meteorological Organization (IRIMO) for providing rainfall data recorded at the Tehran synoptic station. Funding support was provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada

Effects of information and communication technology improvement on revisit intention during Covid-19 Edit Download

Purpose- The present study aims to investigate the interrelationship between information and communication technology (ICT) improvement, destination brand image, destination satisfaction, and destination personality, and how ICT improvement influences tourists\u27 intention to return during the pandemic COVID -19. Design, methodology, approach- The statistical population consisted of Iranian tourists who had stayed in hotels in Iran during CPVID-19. A questionnaire was developed and distributed, and 310 usable questionnaires were received. To test the hypotheses, confirmatory factor analysis was performed using Smart PLS3. Findings- Our results showed that ICT improvement had a significant, positive impact on tourists\u27 revisit intentions and destination brand image during the pandemic COVID -19. Destination brand image also had a significant influence on revisit intention, destination satisfaction, and destination personality. In addition, destination satisfaction and destination personality were significantly related to revisit intention during the pandemic. Originality of the research- This study pioneered the evaluation of ICT in the tourism industry, focusing on the hospitality industry during the pandemic COVID -19. It also examined the direct impact of ICT improvements on revisit intentions during the pandemic. In addition, this study provides evidence for managers to more effectively leverage ICT potential to improve destination brand image and encourage customers to revisit during a pandemic