This paper is dedicated to the implementation of a law of mechanical behavior in the finite element software Cast3M using an open source code generator named Mfront. To do so, an elastoplastic behaviour model has been chosen from existing laws in the literature. Following an implicit discretization, a hardware library corresponding to the isotropic and kinematic strain-hardening model is generated using Mfront. The UMAT computer interface is used to build the library in Cast3M. A validation of the approach has been carried out by comparing the numerical results obtained with the generated hardware library and the equivalent pre-existing library in Cast3M. Simulations in the case of a tensile bar and a perforated plate show almost identical results
For more than one century, Aedes species are supposed to be a reservoir in dengue, yellow fever, rift valley fever and west nile viruses transmission. In this article, we study an infinite dimension ordinary differential equations system that models arbovirus vertical transmission in \textit{Aedes} mosquito. Relying of the positive semigroup theory, we show that the model is well-posed and compute a threshold parameter known as the basic reproduction ratio $R0$. This parameter describes "the average rate of secondary new cases of infected adult females from emergences in a breeding habitat that are produced by an infected adult female via transovarial transmission during its lifetime." In addition, we prove that the solution of the model goes to zero asymptotically if R0<1$, else it has the property of balanced exponential growth. Finally, a climate-environment effects Index on model parameters and a diagram depicting the conditions of arboviruses persistence via Aedes in nature is derived.
In the world of e-learning, learning systems have sought to adapt the user's profile and the content offered to them. However, from the point of view of collaboration between learners based on adaptation to the learner profile, this adaptation has not been sufficiently explored as an important aspect of the e-learning process. Adaptation will allow users with similar or very similar profiles to be grouped together to learn in harmony while maintaining motivation and commitment to learning. This should increase the success rate of learners. This will also allow us to reuse learning paths with good success rates for future recommendations to users with the same profile. In this paper, we focus on this aspect and propose a learning system that controls learning paths adapted to the users' profile and that allows collaborative learning of users in a synchronous way. After an overview of the existing work in the field of adaptive e-learning, we propose an architecture for the piloting of this type of collaborative adaptive learning based on ontologies and orchestrated by a multi-agent system. The latter is responsible for the piloting of learning paths, the recommendation of paths in collaborative or non-collaborative mode through communication between the different agents involved, and the management of events captured by the system.
