Direct determination of the crystal field parameters of Dy, Er and Yb impurities in the skutterudite compound CeFe4_{4}P12_{12} by Electron Spin Resonance

Despite extensive research on the skutterudites for the last decade, their electric crystalline field ground state is still a matter of controversy. We show that Electron Spin Resonance (ESR) measurements can determine the full set of crystal field parameters (CFPs) for the Th cubic symmetry (Im3) of the Ce$_{1-x}$R$_{x}$Fe$_{4}$P$_{12}$ (R = Dy, Er, Yb, $x\lesssim 0.003$) skutterudite compounds. From the analysis of the ESR data the three CFPs, B4c, B6c and B6t were determined for each of these rare-earths at the Ce$^{3+}$ site. The field and temperature dependence of the measured magnetization for the doped crystals are in excellent agreement with the one predicted by the CFPs Bnm derived from ESR.Comment: 7 pages, 5 figures, to appear in PR

A wireless bioimpedance device for abdominal fatness monitoring

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Automated android malware detection using user feedback

The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed in marketplaces that host millions and therefore have their forms of automated malware detection in place to deter malware developers and keep their app store (and reputation) trustworthy. Nevertheless, a non-negligible number of apps can bypass these detectors and remain available in the marketplace for any user to download and install on their device. Current malware detection strategies rely on using static or dynamic app extracted features (or a combination of both) to scale the detection and cover the growing number of apps submitted to the marketplace. In this paper, the main focus is on the apps that bypass the malware detectors and stay in the marketplace long enough to receive user feedback. This paper uses real-world data provided by an app store. The quantitative ratings and potential alert flags assigned to the apps by the users were used as features to train machine learning classifiers that successfully classify malware that evaded previous detection attempts. These results present reasonable accuracy and thus work to help to maintain a user-safe environment.info:eu-repo/semantics/publishedVersio