Tree-level unitarity constraints on the masses of the Higgs bosons in the general Two Higgs Doublet Model (THDM) are studied. We first consider the case where the Higgs potential is invariant under a discrete symmetry transformation, and derive strong constraints on the mass of the lightest CP-even Higgs boson (M h ) as a function of tan β. We then show that the inclusion of the discrete symmetry breaking term weakens the mass bounds considerably. It is suggested that a measurement of M h and tan β may enable discrimination between the two Higgs potentials.

Full one-loop electroweak corrections to the on-shell decay H + → W + A 0 are computed in the framework of models with two Higgs doublets (THDM). Such a decay may be dominant for H ± over a wide range of parameter space relevant at present and future colliders. We show that the corrections may approach 40% and in particular are sensitive to λ 5 , which parametrizes the discrete symmetry breaking term. We suggest that a measurement of the branching ratio of H + → W + A 0 may offer a possibility of measuring the magnitude of λ 5 .

No abstract

Wireless sensor networks have become an emerging research area due to their importance in the present industrial application. The enlargement of network lifetime is the major limitation in WSN. Several routing protocols study the extension of lifespan in WSN. Routing protocols significantly influence on the global of energy consumption for sensors in WSN. It is essential to correct the energy efficiency performance of routing protocol in order to improve the lifetime. The protocols based on clustering are the most routing protocols in WSN to reduce energy consumption. The protocols dedicate to WSN have demonstrated their limitation in expanding the lifetime of the network. In this paper, we present Hybrid SEP protocol : Multi-zonal Fuzzy logic heterogeneous Clustering based on Stable Election Protocol (FMZ-SEP). The FMZ-SEP characterizes by four parameters: WSN segmentation (splitting the WSN into the triangle zones ), the Subtractive Clustering Method to determine a correct number of clusters, the FCM and the SEP protocol. The FMZ-SEP prolong the stability period and extend the lifetime. The simulation results point out that the stability period of FMZ-SEP. FMZ-SEP protocol outperforms of MZ-SEP, FSEP and SEP protocol by improving the network lifetime and the stability period.

Energy efficiency is the most critical challenge in wireless sensor network. The transmission energy is the most consuming task in sensor nodes, specifically in large distances. Clustered routing techniques are efficient approaches used to lower the transmission energy and maximize the network’s lifetime. In this paper, a hybrid clustered routing approach is proposed for energy optimization in WSN. This approach is based on K-Means clustering algorithm and LEACH protocol. The simulation results using MATLAB tool have shown that the proposed hybrid approach outperforms LEACH protocol and optimizes the nodes energy and the network lifetime.

We compute the fermionic radiative contributions to the decay

<span>The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors.</span>

No abstract