Effect of Location Accuracy and Shadowing on the Probability of Non-Interfering Concurrent Transmissions in Cognitive Ad Hoc Networks

Cognitive radio ad hoc systems can coexist with a primary network in a scanning-free region, which can be dimensioned by location awareness. This coexistence of networks improves system throughput and increases the efficiency of radio spectrum utilization. However, the location accuracy of real positioning systems affects the right dimensioning of the concurrent transmission region. Moreover, an ad hoc connection may not be able to coexist with the primary link due to the shadowing effect. In this paper we investigate the impact of location accuracy on the concurrent transmission probability and analyze the reliability of concurrent transmissions when shadowing is taken into account. A new analytical model is proposed, which allows to estimate the resulting secure region when the localization uncertainty range is known. Computer simulations show the dependency between the location accuracy and the performance of the proposed topology, as well as the reliability of the resulting secure region

Prevalence of six viruses in potato seed tubers produced in informal seed system in the North West Region of Cameroon.

Evidence has accumulated that there is a trade-off between benefits and costs associated with rapid growth. A trade-off between growth rates and critical. swimming speed (U-crit) had been also reported to be common in teleost fish. We hypothesize that growth acceleration in the F-3 generation of "all-fish" growth hormone gene (GH) transgenic common carp (Cyprinus carpio L.) would reduce the swimming abilities. Growth and swimming performance between transgenic fish and non-transgenic controls were) compared. The results showed that transgenic fish had a mean body weight 1.4-1.9-fold heavier, and a mean specific growth rate (SGR) value 6%-10% higher than the controls. Transgenic fish, however, had a mean absolute U-crit (cm/s) value 22% or mean relative Ucrit (BL/s) value 24% lower than the controls. It suggested that fast-growing "all-fish" GH-transgenic carp were inferior swimmers. It is also supported that there was a trade-off between growth rates and swimming performance, i.e. faster-growing individuals had lower critical swimming speed

A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems

In this work, we present an extension of genetic algorithm (GA) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a lower-dimensional space. In many cases, GA requires in fact more function evaluations than other optimization methods to converge to the global optimum. Thus, complex and high-dimensional functions can end up extremely demanding (from the computational point of view) to be optimized with the standard algorithm. To address this issue, we propose to linearly map the input parameter space of the original function onto its AS before the evolution, performing the mutation and mate processes in a lower-dimensional space. In this contribution, we describe the novel method called ASGA, presenting differences and similarities with the standard GA method. We test the proposed method over n-dimensional benchmark functions-Rosenbrock, Ackley, Bohachevsky, Rastrigin, Schaffer N. 7, and Zakharov-and finally we apply it to an aeronautical shape optimization problem

A dialogue between mathematics education and special education: ethics, inclusion and differentiation for all

International audienceEthical issues play an important role in moulding the philosophy of mathematics education. The present study spells out ethical features of mathematical learning in terms of inclusion. We present the OPEN-MATH project that aims at accomplishing inclusive mathematics learning environments and a teaching learning model based in such a framework

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD