Publications by authors named "Nicolas Poilvert"

Article Synopsis
  • A recent study tested a machine-learning algorithm to assess left ventricular ejection fraction (EF) using various echocardiographic views commonly available in point-of-care settings, as obtaining standard views can be challenging.
  • The study involved comparing automated EF measurements against experienced echocardiographers' assessments from clinical examinations and showed good agreement, with the algorithm's accuracy being comparable to that of physicians.
  • The results indicate that this algorithm can effectively classify LV function in a portable imaging setup, making it a valuable tool for point-of-care evaluations.
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Controlling the thermal conductivity of semiconductors is of practical interest in optimizing the performance of thermoelectric and phononic devices. The insertion of inclusions of nanometer size in a semiconductor is an effective means of achieving such control; it has been proposed that the thermal conductivity of silicon could be reduced to 1 W/m/K using this approach and that a minimum in the heat conductivity would be reached for some optimal size of the inclusions. Yet the experimental verification of this design rule has been limited.

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Background: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients.

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Accurate and efficient approaches to predict the optical properties of organic semiconducting compounds could accelerate the search for efficient organic photovoltaic materials. Nevertheless, predicting the optical properties of organic semiconductors has been plagued by the inaccuracy or computational cost of conventional first-principles calculations. In this work, we demonstrate that orbital-dependent density-functional theory based upon Koopmans' condition [Phys.

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We propose several covalent functionalizations for carbon nanotubes that display switchable on/off conductance in metallic tubes. The switching action is achieved by reversible control of bond-cleavage chemistry in [1 + 2] cycloadditions via the sp(3) ⇌ sp(2) rehybridization that it induces; this leads to remarkable changes of conductance even at very low degrees of functionalization. Reversible bond-cleavage chemistry is achieved by identifying addends that provide optimal compensation between the bond-preserving through-space π orbital interactions with the tube against the bond-breaking strain energy of the cyclopropane moiety.

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