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In order to stay up-to-date in the nursing field, nurses must pursue continuous education. This study examined nurses’ motivations to take part in continuing education activities by conducting a survey amongst a sample of nurses employed in two hospitals in south-east Morocco. The survey was carried out using a two-part self-administered questionnaire. Out of 144 nurses who received the questionnaire, 118 (84%) replied. The results showed that the majority of nurses have never participated in a continuing education session in the last 3 years. Respondents’ motivation for continuing education was mainly intrinsic, with the emphasis on factors related to professional competence and knowledge seeking. The use of such results can therefore be of great use to both managers and practitioners in planning continuing education programmes. However, it is desirable that more studies take place to investigate further the issue of continuing education with the aim of promoting and developing professional nursing skills.

Location-based social networks (LBSNs) have witnessed a great expansion as an attractive form of social media. LBSNs allow users to “check-in” at geographical locations and share this information with friends. Indeed, with the spatial, temporal and social aspects of user patterns provided by LBSNs data, researchers have a promising opportunity for understanding human mobility dynamics, with the purpose of designing new generation mobile applications, including context-aware advertising and city-wide sensing applications. In this paper, the authors introduce a learning based random walk model (LBRW) combining user interests and “mobility homophily” for location recommendation in LBSNs. These properties are observed from a real-world Location-Based Social Networks (LBSNs) dataset. The authors present experimental evidence that validates LBRW and demonstrates the power of these inferred properties in improving location recommendation performance.

Understanding human mobility dynamics is of an essential importance to today mobile applications, including context-aware advertising and city wide sensing applications. Recently, Location-based social networks (LBSNs) have attracted important researchers' efforts, to investigate spatial, temporal and social aspects of user patterns. LBSNs allow users to "check-in" at geographical locations and share this information with friends. In this paper, analysis of check-ins data provided by Foursquare, the online location-based social network, allows us to construct a set of features that capture: spatial, temporal and similarity characteristics of user mobility. We apply this knowledge to location prediction problem, and combine these features in supervised learning for future location prediction. We find that the supervised classifier based on the combination of multiple features offers reasonable accuracy.

In this paper, we study dynamic robust power allocation strategies under the imperfectness of the channel state information at the transmitters. Considering unknown payoff functions at the transmitters, we propose an heterogeneous Delayed COmbined fully DIstributed Payoff and Strategy Reinforcement Learning (Delayed-CODIPAS-RL) in which each transmitter learns its payoff function as well as its associated optimal strategies in the long-term. We show that equilibrium power allocations can be obtained using the multiplicative weighted imitative CODIPAS-RLs and Bush-Mosteller based CODIPAS-RL. We also show almost sure convergence to the set of global optima for specific scenarios.