Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

Desirable combination of weather temperature, relative humidity, air movement and mean radiant temperature, present a certain zone which provides thermal comfort for the majority of people. One of the most important issues, which should be considered before building a construction, is the accurate estimation of the thermal comfort within the designed building. Thus, evaluation of thermal condition of a building in design stage will cause an opportunity to change and modify design elements and materials. In this paper, an attempt has been made to discuss about effective parameters that influence human physical comfort and then four different procedures have been presented to predicting thermal comfort zone.

Initially, in vitro fertilization (IVF) was solely performed in couples with bilateral tubal occlusion. In 1992 intracytoplasmic sperm injection (ICSI) was discovered and initiated in couples with severe male subfertilty. Later on IVF/ICSI was also applied in couples who did not suffer from sterility, such as couples with cervical hostility, failed ovulation induction, endometriosis, unilateral tubal pathology or even unexplained subfertility. The major difference between the original indication and the indications for which IVF is conducted nowadays is that the couples with bilateral tubal pathology or severe male subfertility are sterile and have a (near) zero chance of natural conception and completely depend on IVF/ICSI for a pregnancy, while the other couples are subfertile: they do have chances of natural conception, which may or may not be better than with IVF.Despite the lack of evidence that IVF is effective in subfertile couples, IVF is often considered as a last resort for all couples regardless of the etiology of their subfertility.Contrary to the perception of many, IVF does not guarantee success; almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. Subfertile couples should therefore be well informed about the chances of success with IVF before starting their first or before continuing with a new IVF cycle.Based on a couple’s specific probability, one should decide whether the chances of success with IVF outweigh the burden, risks and costs of the treatment.To do so, prediction models have been developed. Most existing models are of limited use for several reasons.They were developed before current clinical and laboratory protocols were established. They do not include the transfer of frozen-thawed embryos. They calculate pregnancy chances only for the first IVF cycle or after one failed IVF.We developed a model that would calculate pregnancy chances during the complete IVF procedure, after failed cycles, and that included pregnancies after fresh and frozen-thawed embryo transfer which performed well after internal and external, temporal validation.If couples start or continue with IVF, the aim should be to achieve optimal pregnancy chances with a low risk multiple pregnancies. The optimal embryo transfer strategy would be a ‘‘individualized embryo transfer strategy’’ that takes the woman’s prognostic profile and embryo characteristics into account; a prediction model that is able to select which and how many embryo (s) should be transferred to obtain optimal ongoing pregnancy rates with low multiple pregnancy rates. To develop such a model, we first constructed a model that was able to rank embryos on day 3 after oocyte retrieval based on their ongoing implantation potential. We then developed an embryo transfer model that consisted of two variables: one variable being the sum of all coefficients of the IVF model and the second variable being the sum of all coefficients of the embryo implantation model. These models will be presented.With help of these prediction models a more uniform treatment strategy is possible, and also a more ‘patient tailored’ treatment.

The spatial prediction of an unknown quantity at a specific site is one of the most important topics in the fuzzy spatial analysis. Under the assumption that covariance is known, optimal prediction and mean square error of predictor are determinable by using fuzzy kriging methods. When the parameters of the parametric covariance function are unknown, their estimates are usually replaced in optimal prediction as real values. But, determination of this predictor and its mean square error are usually difficult. Therefore, to solve this problem, in this paper the Bayesian approach is used to extend the fuzzy kriging to a new method, namely Bayesian fuzzy kriging. Then, in an applied example, its accuracy is compared with other spatial predictors.