A NOVEL MODEL AND TOOL FOR ENERGY RENOVATION PLANNING IN FRENCH RESIDENTIAL BUILDINGS AND DISTRICTS

Energy renovation of existing buildings is important for energy consumption reduction. In fact, it attracted the interest of governments in several countries for its effectiveness (e.g., 38 % consumption reduction by 2020 predicted in France). To achieve such rates, major incentivizing measures were taken by governments to facilitate the funding of energy-oriented renovation projects for final users (e.g., households, communities). Despite all these efforts, a lot of obstacles are yet to be overcome like the lack of interest and involvement of the population, the lack of understanding of the economic equation for renovation, unawareness of governmental aids and support. In fact, most of the population does not fully understand the long-term investment benefits of renovation and look at short term-centered benefits. By taking this into account, the aim of our study is to design, develop and implement a simulation model and decisionmaking tool to assist final users. The first objective of this tool is to shed the light on the advantages and the benefits of renovation to achieve a maximum awareness. To this end, we studied and highlighted three types of incentives: economical, ecological and comfort. The second objective is related to the technical aspects of the project, where users simulate one or several renovations with different characteristics such as insulation materials, space heater, glazing type. Based on the selected parameters, users will be provided with the cost of renovation works, and achievable yearly savings (energy, money, CO2, etc.). Consequently, the user can make the right decision that suits his needs

MHD heat transfer in W-shaped inclined cavity containing a porous medium saturated with Ag/Al2O3 hybrid nanofluid in the presence of uniform heat generation/absorption

© 2020 by the Authors. In this paper, a 2D numerical study of natural convection heat transfer in a W-shaped inclined enclosure with a variable aspect ratio was performed. The enclosure contained a porous medium saturated with Ag/Al2O3 hybrid nanofluid in the presence of uniform heat generation or absorption under the effect of a uniform magnetic field. The vertical walls of the enclosure were heated differentially; however, the top and bottom walls were kept insulated. The governing equations were solved with numerical simulation software COMSOL Multiphysics which is based on the finite element method. The results showed that the convection heat transfer was improved with the increase of the aspect ratio; the average Nusselt number reached a maximum for an aspect ratio (AR) = 0.7 and the effect of the inclination was practically negligible for an aspect ratio of AR = 0.7. The maximum heat transfer performance was obtained for an inclination of ω = 15 and the minimum is obtained for ω = 30. The addition of composite nanoparticles ameliorated the convection heat transfer performance. This effect was proportional to the increase of Rayleigh and Darcy numbers, the aspect ratio and the fraction of Ag in the volumetric fraction of nanoparticles

Towards securing machine learning models against membership inference attacks

From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress in the research works proposing defence techniques against ML security and privacy attacks. The relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks (MIA) and the related countermeasures. In this paper, we introduce a countermeasure against membership inference attacks (MIA) on Conventional Neural Networks (CNN) based on dropout and L2 regularization. Through experimental analysis, we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the model. Indeed, using CNN model training on two datasets CIFAR-10 and CIFAR-100, we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different classifiers. Moreover, we present a solution to achieve a trade-off between the performance of the model and the mitigation of MIA attack

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease

Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study. We inferred topological changes in functional connectivity between 182 regions within cortico-basal ganglia networks using graph theory measures. We found significant differences for global graph theory measures in HD but not in preHD. The average shortest path length (L) decreased, which indicated a change toward the random network topology. HD patients also demonstrated increases in degree k, reduced betweeness centrality bc and reduced clustering C. Changes predominated in the sensorimotor network for bc and C and were observed in all circuits for k. Hubs were reduced in preHD and no longer detectable in HD in the sensorimotor and associative networks. Changes in graph theory metrics (L, k, C and bc) correlated with four clinical and cognitive measures (symbol digit modalities test, Stroop, Burden and UHDRS). There were no changes in graph theory metrics across sessions, which suggests that these measures are not reliable biomarkers of longitudinal changes in HD. preHD is characterized by progressive decreasing hub organization, and these changes aggravate in HD patients with changes in local metrics. HD is characterized by progressive changes in global network interconnectivity, whose network topology becomes more random over time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc

RF assisted switching in magnetic Josephson junctions

We test the effect of an external RF field on the switching processes of magnetic Josephson junctions (MJJs) suitable for the realization of fast, scalable cryogenic memories compatible with Single Flux Quantum logic. We show that the combined application of microwaves and magnetic field pulses can improve the performances of the device, increasing the separation between the critical current levels corresponding to logical "0" and "1." The enhancement of the current level separation can be as high as 80% using an optimal set of parameters. We demonstrate that external RF fields can be used as an additional tool to manipulate the memory states, and we expect that this approach may lead to the development of new methods of selecting MJJs and manipulating their states in memory arrays for various applications

Magnetization dynamics in dilute Pd<inf>1-</inf><inf>x</inf>Fe<inf>x</inf> thin films and patterned microstructures considered for superconducting electronics

© 2016 Author(s).Motivated by recent burst of applications of ferromagnetic layers in superconducting digital and quantum elements, we study the magnetism of thin films and patterned microstructures of Pd0.99Fe0.01. In this diluted ferromagnetic system, a high-sensitivity ferromagnetic resonance (FMR) experiment reveals spectroscopic signatures of re-magnetization and enables the estimation of the saturation magnetization, the anisotropy field, and the Gilbert damping constant. The detailed analysis of FMR spectra links the observed unexpectedly high reduced anisotropy field (0.06-0.14) with the internal anisotropy, points towards a cluster nature of the ferromagnetism, and allows estimating characteristic time scale for magnetization dynamics in Pd-Fe based cryogenic memory elements to (3 - 5) × 10 - 9 s

PhytAMP: a database dedicated to antimicrobial plant peptides

Plants produce small cysteine-rich antimicrobial peptides as an innate defense against pathogens. Based on amino acid sequence homology, these peptides were classified mostly as α-defensins, thionins, lipid transfer proteins, cyclotides, snakins and hevein-like. Although many antimicrobial plant peptides are now well characterized, much information is still missing or is unavailable to potential users. The compilation of such information in one centralized resource, such as a database would therefore facilitate the study of the potential these peptide structures represent, for example, as alternatives in response to increasing antibiotic resistance or for increasing plant resistance to pathogens by genetic engineering. To achieve this goal, we developed a new database, PhytAMP, which contains valuable information on antimicrobial plant peptides, including taxonomic, microbiological and physicochemical data. Information is very easy to extract from this database and allows rapid prediction of structure/function relationships and target organisms and hence better exploitation of plant peptide biological activities in both the pharmaceutical and agricultural sectors. PhytAMP may be accessed free of charge at http://phytamp.pfba-lab.org

Micromagnetic modeling of critical current oscillations in magnetic Josephson junctions

© 2016 American Physical Society.In this work we propose and explore an effective numerical approach for investigation of critical current dependence on applied magnetic field for magnetic Josephson junctions with in-plane magnetization orientation. This approach is based on micromagnetic simulation of the magnetization reversal process in the ferromagnetic layer with introduced internal magnetic stiffness and subsequent reconstruction of the critical current value using total flux or reconstructed actual phase difference distribution. The approach is flexible and shows good agreement with experimental data obtained on Josephson junctions with ferromagnetic barriers. Based on this approach we have obtained a critical current dependence on applied magnetic field for rectangular magnetic Josephson junctions with high size aspect ratio. We have shown that the rectangular magnetic Josephson junctions can be considered for application as an effective Josephson magnetic memory element with the value of critical current defined by the orientation of magnetic moment at zero magnetic field. An impact of shape magnetic anisotropy on critical current is revealed and discussed. Finally, we have considered a curling magnetic state in the ferromagnetic layer and demonstrated its impact on critical current