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.

Introduction: Choosing the right type of device is important to support users of Health Information Systems. In addition to cost, durability and simplicity, staffing levels and hospital workflows affect in the selected. The purpose of this study was to identify the preferences of users in device selection.Methods: This applied research with qualitative method, Focus group, was conducted in 2014. Population research are users of HIS. They have been divided into three distinct groups based on their computer skills and medical knowledge. The sixteen users, were invited to three focus group meetings and have been asked to explain their criteria for selection of the device. Questionnaires were also used in order to compare the results of discussions quantitatively. The reliability and validity of the questionnaire were confirmed by Cronbach’s alpha (0.86). The analysis was performed using descriptive statistics.Results: The mean response of participants shows that end-users with high level of medical knowledge and computer skills prefer to use tablets (3.75) and then PCs (3.67). Medical staff without computer skills selected tablets (4), workstation on wheels (WOW) (2.5), wall mounted workstation (2.5) and PCs (2.25). Ultimately members with skills in using the computer and lack of medical knowledge, selected PCs (3.37), tablets (3.29), Personal Digital Assistant (PDA) (3.17), wall mounted workstation (4.2) and WOW (2.67).Conclusion: This study showed that the best device for the medical staff is Tablet and PC is the best device for administrative staff.

In the current era of online learning, the recommendation system for the eLearning process is quite important. Since the COVID-19 pandemic, eLearning has undergone a complete transformation. Existing eLearning Recommendation Systems worked on collaborative filtering or content-based filtering based on historical data, students’ previous grade, results, or user profiles. The eLearning system selected courses based on these parameters in a generalized manner rather than on a personalized basis. Personalized recommendations, information relevancy, choosing the proper course, and recommendation accuracy are some of the issues in eLearning recommendation systems. In this paper, existing conventional eLearning and course recommendation systems are studied in detail and compared with the proposed approach. We have used, the dataset of user Profile and user Rating for a recommendation of the course. K Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Nave Bayes, Linear Regression, Linear Discriminant Analysis, and Neural Network were among the Machine Learning techniques explored and deployed. The accuracy achieved for all these algorithms ranges from 0. 81 to 0. 97. The proposed algorithm uses a hybrid approach by combining collaborative filtering and deep learning. We have improved accuracy to 0. 98 which indicate that the proposed model can provide personalized and accurate eLearning recommendation for the individual user.

This paper concerns locational finding in broadband wireless networks in corporating OFDMA technique at the plysical layer. In this regard, a novel estimation method is proposed to find the position of active users. This method makes use of the power spectral density of the received signal from a specific user to compare its likelihood to that of some reference points. Despite the conventional method which performs well in a single path environment, the proposed method not only robusts in a muli-path environment, but also it inherently incorporates path delay spreads to find the location of exiting users. Simulation results indicate that the mean estimation error in the wimax network operation is around 11 meters at 3.5 Ghz with 5 Mhz bandwidth and when reference points are 26.4 meters apart relying on SUI model.

One of the most important parts of the vessels is its diesel engines. These engines are in charge of various operations like generating electricity power, propulsion power, fire pumps, and etc. Therefore, due to the unnecessary expenses may result from choosing the wrong engine, engine selection is of a great importance and this paper tries to assess the important and effective factors in choosing the right engines.The engine selection procedure is presented under the following headings:Fuel consumption and engine oilThe volume occupied by the engineEngine RepairsThe rapid reaction of the engine in the case of increasing the pitch or in moving back in direct drive vesselsBeing consistent with international maritime laws and conventionsThe ability to work with an alternative fuel in deep-sea vesselsBeing user friendly and the ability to easy and simple repairs of enginesThe amount of engine vibration and noise resulting from itOther important factors.