Polytechnic University of Catalonia

About: Polytechnic University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Finite element method & Population. The organization has 16006 authors who have published 45325 publications receiving 949306 citations. The organization is also known as: UPC - BarcelonaTECH & Technical University of Catalonia.

Barocaloric and magnetocaloric effects in Fe49Rh51

Abstract: We report on calorimetry under applied hydrostatic pressure and magnetic field at the antiferromagnetic-ferromagnetic (AFM/FM) transition of Fe49Rh51. Results demonstrate the existence of a giant barocaloric effect in this alloy, a functional property that adds to the magnetocaloric and elastocaloric effects previously reported for this alloy. All caloric effects originate from the AFM/FM transition which encompasses changes in volume, magnetization, and entropy. The strong sensitivity of the transition temperatures to both hydrostatic pressure and magnetic field confers to this alloy outstanding values for the barocaloric and magnetocaloric strengths (|?S|/?p ~ 12 J kg-1K-1kbar-1 and |?S|/µ0?H~ 12 J kg-1K-1T-1). Both barocaloric and magnetocaloric effects have been found to be reproducible upon pressure and magnetic field cycling. Such a good reproducibility and the large caloric strengths make Fe-Rh alloys particularly appealing for solid-state cooling technologies at weak external stimuli.

Deep learning for freezing of gait detection in Parkinsons disease patients in their homes using a waist-worn inertial measurement unit

Abstract: Among Parkinsons disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patients condition and the symptoms characteristics, while it could enable non-pharmacologic support based on rhythmic cues.This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system.The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.

Solving (Weighted) Partial MaxSAT through Satisfiability Testing

Abstract: Recently, Fu and Malik described an unweighted Partial MaxSAT solver based on successive calls to a SAT solver. At the k th iteration the SAT solver tries to certify that there exist an assignment that satisfies all but k clauses. Later Marques-Silva and Planes implemented and extended these ideas. In this paper we present and implement two Partial MaxSAT solvers and the weighted variant of one of them. Both are based on Fu and Malik ideas. We prove the correctness of our algorithm and compare our solver with other (Weighted) MaxSAT and (Weighted) Partial MaxSAT solvers.