Abstract-learning analytics is an emerging discipline focused on the measurement, collection, analysis and reporting of learner interaction data through the E-learning contents. Serious game provides a potential source for relevant educational user data; it can propose an interactive environment for training and offer an effective learning process. This paper presents methods and approaches of educational data mining such as EM and K-Means to discuss the learning analytics through serious games, and then we provide an analysis of the player experience data collected from the educational game "ELISA" used to teach students of biology the immunological technique for determination of ANTI-HIV antibodies. Finally, we propose critically evaluation of our results including the limitations of our study and making suggestions for future research that links learning analytics and serious gaming.

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This paper is a comparative study of several antennas commonly used in cellular telephones. These include a monopole and a patch antenna. Each one of these structures is modeled and numerically tested using HFSS code. The testing procedure involves antenna simulation in the proximity of the human head. The behaviour of each antenna is evaluated for variable distances from the head geometry. Continuous waveform, representative of the sources used in mobile telephones, (250 mW) is used as the form of the antenna excitation. The SAR levels for the head tissues are calculated for and with accordance to the two currently accepted standards: Federal Communications Commission (FCC) and International Commission on NonIonizing Radiation Protection (ICNIRP). The computed SAR levels within each of the considered tissues vary for the two antennas under investigation.

Too many researches have been done using artificial immune systems AIS to solve intrusion detection problems due to several reasons. The self and non-self model based on the Negative Selection Algorithm NSA is the dominant model since it is adopted by the vast majority of these researches. However, this model has some problems especially in terms of scalability and coverage. This paper tries to exploit some interesting concepts proposed by the new danger theory to overcome the problems associated with the self and non-self model. That by improving NSA in order to achieve better detection rates by integrating the basic danger concepts. In this approach, the intrusion detection is related to the damage that can occur in the system and that can be caused by both external elements such as internal elements. The proposed algorithm integrates and combines the basic concepts of intrusion detection systems IDS based on the role of T cells described by the negative selection algorithm, with those inspired by the role of dendritic cells to process the alarm signals and to judge thereafter whether there is presence of a dangerous element or not.