Smart Vehicle to Grid Interface Project: Electromobility Management System Architecture and Field Test Results

This paper presents and discusses the electromobility management system developed in the context of the SMARTV2G project, enabling the automatic control of plug-in electric vehicles' (PEVs') charging processes. The paper describes the architecture and the software/hardware components of the electromobility management system. The focus is put in particular on the implementation of a centralized demand side management control algorithm, which allows remote real time control of the charging stations in the field, according to preferences and constraints expressed by all the actors involved (in particular the distribution system operator and the PEV users). The results of the field tests are reported and discussed, highlighting critical issues raised from the field experience.Comment: To appear in IEEE International Electric Vehicle Conference (IEEE IEVC 2014

Identification of candidate host serum and saliva biomarkers for a better diagnosis of active and latent tuberculosis infection

In our work, we aim to identify new candidate host biomarkers to discriminate between active TB patients (n = 28), latent infection (LTBI; n = 27) and uninfected (NoTBI; n = 42) individuals. For that, active TB patients and their contacts were recruited that donated serum and saliva samples. A multiplex assay was performed to study the concentration of different cytokines, chemokines and growth factors. Proteins with significant differences between groups were selected and logistic regression and the area under the ROC curve (AUC) was used to assess the diagnostic accuracy. The best marker combinations that discriminate active TB from NoTBI contacts were [IP-10 + IL-7] in serum and [Fractalkine + IP-10 + IL-1alpha + VEGF] in saliva. Best discrimination between active TB and LTBI was achieved using [IP-10 + BCA-1] in serum (AUC = 0.83) and IP-10 in saliva (p = 0.0007; AUC = 0.78). The levels of TNFalpha (p = 0.003; AUC = 0.73) in serum and the combination of [Fractalkine+IL-12p40] (AUC = 0.83) in saliva, were able to differentiate between NoTBI and LTBI contacts. In conclusion, different individual and combined protein markers could help to discriminate between active TB and both uninfected and latently-infected contacts. The most promising ones include [IP-10 + IL-7], [IP-10 + BCA-1] and TNFalpha in serum and [Fractalkine + IP-10 + IL-1alpha + VEGF], IP-10 and [Fractalkine+IL-12p40] in saliva