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The problem associated with mixtures of fillers and polymers is that they result in mechanical degradation of the material (polymer) as the filler content increases. This problem will increase the weight of the material and manufacturing cost. For this reason, experimentation on the electrical conductivities of the polymer-composites (PCs) is not enough to research their electrical properties; models have to be adopted to solve the encountered challenges. Hitherto, several models by previous researchers have been developed and proposed, with each utilizing different design parameters. It is imperative to carry out analysis on these models so that the suitable one is identified. This paper indeed carried out a comprehensive parametric analysis on the existing electrical conductivity models for polymer composites. The analysis involves identification of the parameters that best predict the electrical conductivity of polymer composites for energy storage, viz: (batteries and capacitor), sensors, electronic device components, fuel cell electrodes, automotive, medical instrumentation, cathode scanners, solar cell, and military surveillance gadgets applications. The analysis showed that the existing models lack sufficient parametric ability to determine accurately the electrical conductivity of polymer-composites.

International audience Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Rie-mannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.

International audience ; In this study, the importance of renewable energy as a complement to meeting the energy demand in Africa was investigated. In most African countries, the larger percentage of power generation is from fossil fuel-based energy sources, and without a doubt, this can be complemented with renewable energy to meet the demand per country and the global interest on climate change. Nigeria, Cameroon, Ghana and South Africa are among the leading African countries that are blessed with an abundance of renewable energy resources, which have the potentials to improve their respective energy generation potentials; thereby, contributing to the socio-economic growth, promote industrialization, reduce global warming and create green jobs. Despite the abundant renewable energy sources in these countries, they are yet to fully harness the potentials, therefore, resulting in their deficiencies in contributing to the electrical power generation per country. The bulk of the problems associated with renewable energy in African countries are: little interest and lopsided policy on renewable energy, enforcing existing policies, high renewable energy tariff, and lack of adequate infrastructure. The current study discusses the ways by which conscious decisions and policy of governments, alongside the stakeholders' dedication can ensure sustainable energy supplies. Based on the findings of the study, renewable energy-based power generation can greatly reduce greenhouse gases. Also, subsidizing the tariff levied on renewable can promote energy generation leading to job creation through energy generation from renewable resources.

International audience ; In this study, the importance of renewable energy as a complement to meeting the energy demand in Africa was investigated. In most African countries, the larger percentage of power generation is from fossil fuel-based energy sources, and without a doubt, this can be complemented with renewable energy to meet the demand per country and the global interest on climate change. Nigeria, Cameroon, Ghana and South Africa are among the leading African countries that are blessed with an abundance of renewable energy resources, which have the potentials to improve their respective energy generation potentials; thereby, contributing to the socio-economic growth, promote industrialization, reduce global warming and create green jobs. Despite the abundant renewable energy sources in these countries, they are yet to fully harness the potentials, therefore, resulting in their deficiencies in contributing to the electrical power generation per country. The bulk of the problems associated with renewable energy in African countries are: little interest and lopsided policy on renewable energy, enforcing existing policies, high renewable energy tariff, and lack of adequate infrastructure. The current study discusses the ways by which conscious decisions and policy of governments, alongside the stakeholders' dedication can ensure sustainable energy supplies. Based on the findings of the study, renewable energy-based power generation can greatly reduce greenhouse gases. Also, subsidizing the tariff levied on renewable can promote energy generation leading to job creation through energy generation from renewable resources.